Tag Archives: waf

Building even faster interpreters in Rust

Post Syndicated from Zak Cutner original https://blog.cloudflare.com/building-even-faster-interpreters-in-rust/

Building even faster interpreters in Rust

Building even faster interpreters in Rust

At Cloudflare, we’re constantly working on improving the performance of our edge — and that was exactly what my internship this summer entailed. I’m excited to share some improvements we’ve made to our popular Firewall Rules product over the past few months.

Firewall Rules lets customers filter the traffic hitting their site. It’s built using our engine, Wirefilter, which takes powerful boolean expressions written by customers and matches incoming requests against them. Customers can then choose how to respond to traffic which matches these rules. We will discuss some in-depth optimizations we have recently made to Wirefilter, so you may wish to get familiar with how it works if you haven’t already.

Minimizing CPU usage

As a new member of the Firewall team, I quickly learned that performance is important — even in our security products. We look for opportunities to make our customers’ Internet properties faster where it’s safe to do so, maximizing both security and performance.

Our engine is already heavily used, powering all of Firewall Rules. But we have bigger plans. More and more products like our Web Application Firewall (WAF) will be running behind our Wirefilter-based engine, and it will become responsible for eating up a sizable chunk of our total CPU usage before long.

How to measure performance?

Measuring performance is a notoriously tricky task, and as you can probably imagine trying to do this in a highly distributed environment (aka Cloudflare’s edge) does not help. We’ve been surprised in the past by optimizations that look good on paper, but, when tested out in production, just don’t seem to do much.

Our solution? Performance measurement as a service — an isolated and reproducible benchmark for our Firewall engine and a framework for engineers to easily request runs and view results. It’s worth noting that we took a lot of inspiration from the fantastic Rust Compiler benchmarks to build this.

Building even faster interpreters in Rust
Our benchmarking framework, showing how performance during different stages of processing Wirefilter expressions has changed over time [1].

What to measure?

Our next challenge was to find some meaningful performance metrics. Some experimentation quickly uncovered that time was far too volatile a measure for meaningful comparisons, so we turned to hardware counters [2]. It’s not hard to find tools to measure these (perf and VTune are two such examples), although they (mostly) don’t allow control over which parts of the program are recorded. In our case, we wished to individually record measurements for different stages of filter processing — parsing, compilation, analysis, and execution.

Once again we took inspiration from the Rust compiler, and its self-profiling options, using the perf_event_open API to record counters from inside our binary. We then output something like the following, which our framework can easily ingest and store for later visualization.

Building even faster interpreters in Rust
Output of our benchmarks in JSON Lines format, showing a list of recordings for each combination of hardware counter and Wirefilter processing stage. We’ve used 10 repeats here for readability, but we use around 20, in addition to 5 warmup rounds, within our framework.

Whilst we mainly focussed on metrics relating to CPU usage, we also use a combination of getrusage and clear_refs to find the maximum resident set size (RSS). This is useful to understand the memory impact of particular algorithms in addition to CPU.

But the challenge was not over. Cloudflare’s standard CI agents use virtualization and sandboxing for security and convenience, but this makes accessing hardware counters virtually impossible. Running our benchmarks on a dedicated machine gave us access to these counters, and ensured more reproducible results.

Speeding up the speed test

Our benchmarks were designed from the outset to take an important place in our development process. For instance, we now perform a full benchmark run before releasing each new version to detect performance regressions.

But with our benchmarks in place, it quickly became clear that we had a problem. Our benchmarks simply weren’t fast enough — at least if we wanted to complete them in less than a few hours! The problem was we have a very large number  of filters. Since our engine would never usually execute requests against this many filters at once it was proving incredibly costly. We came up with a few tricks to cut this down…

  • Deduplication. It turns out that only around a third of filters are structurally unique (something that is easy to check as Wirefilter can helpfully serialize to JSON). We managed to cut down a great deal of time by ignoring duplicate filters in our benchmarks.
  • Sampling. Still, we had too many filters and random sampling presented an easy solution. A more subtle challenge was to make sure that the random sample was always the same to maintain reproducibility.
  • Partitioning. We worried that deduplication and sampling would cause us to miss important cases that are useful to optimize. By first partitioning filters by Wirefilter language feature, we can ensure we’re getting a good range of filters. It also helpfully gives us more detail about where specifically the impact of a performance change is.

Most of these are trade-offs, but very necessary ones which allow us to run continual benchmarks without development speed grinding to a halt. At the time of writing, we’ve managed to get a benchmark run down to around 20 minutes using these ideas.

Optimizing our engine

With a benchmarking framework in place, we were ready to begin testing optimizations. But how do you optimize an interpreter like Wirefilter? Just-in-time (JIT) compilation, selective inlining and replication were some ideas floating around in the word of interpreters that seemed attractive. After all, we previously wrote about the cost of dynamic dispatch in Wirefilter. All of these techniques aim to reduce that effect.

However, running some real filters through a profiler tells a different story. Most execution time, around 65%, is spent not resolving dynamic dispatch calls but instead performing operations like comparison and searches. Filters currently in production tend to be pretty light on functions, but throw in a few more of these and even less time would be spent on dynamic dispatch. We suspect that even a fair chunk of the remaining 35% is actually spent reading the memory of request fields.

FunctionCPU time
`matches` operator0.6%
`in` operator1.1%
`eq` operator11.8%
`contains` operator51.5%
Everything else35.0%
Breakdown of CPU time while executing a typical production filter.

An adventure in substring searching

By now, you shouldn’t be surprised that the contains operator was one of the first in line for optimization. If you’ve ever written a Firewall Rule, you’re probably already familiar with what it does — it checks whether a substring is present in the field you are matching against. For example, the following expression would match when the host is “example.com” or “www.example.net”, but not when it is “cloudflare.com”. In string searching algorithms, this is commonly referred to as finding a ‘needle’ (“example”) within a ‘haystack’ (“example.com”).

http.host contains “example”

How does this work under the hood? Ordinarily, we may have used Rust’s `String::contains` function but Wirefilter also allows raw byte expressions that don’t necessarily conform to UTF-8.

http.host contains 65:78:61:6d:70:6c:65

We therefore used the memmem crate which performs a two-way substring search algorithm on raw bytes.

Sounds good, right? It was, and it was working pretty well, although we’d noticed that rewriting `contains` filters using regular expressions could bizarrely often make them faster.

http.host matches “example”

Regular expressions are great, but since they’re far more powerful than the `contains` operator, they shouldn’t be faster than a specialized algorithm in simple cases like this one.

Something was definitely up. It turns out that Rust’s regex library comes equipped with a whole host of specialized matchers for what it deems to be simple expressions like this. The obvious question was whether we could therefore simply use the regex library. Interestingly, you may not have realized that the popular ripgrep tool does just that when searching for fixed-string patterns.

However, our use case is a little different. Since we’re building an interpreter (and we’re using dynamic dispatch in any case), we would prefer to dispatch to a specialized case for `contains` expressions, rather than matching on some enum deep within the regex crate when the filter is executed. What’s more, there are some pretty cool things being done to perform substring searching that leverages SIMD instruction sets. So we wired up our engine to some previous work by Wojciech Muła and the results were fantastic.

BenchmarkImprovement
Expressions using `contains` operator72.3%
‘Simple’ expressions0.0%
All expressions31.6%
Improvements in instruction count using Wojciech Muła’s sse4-strstr library over the memmem crate with Wirefilter.

I encourage you to read more on “Algorithm 1”, which we used, but it works something like this (I’ve changed the order a little to help make it clearer). It’s worth reading up on SIMD instructions if you’re unfamiliar with them — they’re the essence behind what makes this algorithm fast.

  1. We fill one SIMD register with the first byte of the needle being searched for, simply repeated over and over.
  2. We load as much of our haystack as we can into another SIMD register and perform a bitwise equality operation with our previous register.
  3. Now, any position in the resultant register that is 0 cannot be the start of the match since it doesn’t start with the same byte of the needle.
  4. We now repeat this process with the last byte of the needle, offsetting the haystack, to rule out any positions that don’t end with the same byte as the needle.
  5. Bitwise ANDing these two results together, we (hopefully) have now drastically reduced our potential matches.
  6. Each of the remaining potential matches can be checked manually using a memcmp operation. If we find a match, then we’re done.
  7. If not, we continue with the next part of our haystack and repeat until we’ve checked the entire thing.

When it goes wrong

You may be wondering what happens if our haystack doesn’t fit neatly into registers. In the original algorithm, nothing. It simply continues reading into the oblivion after the end of the haystack until the last register is full, and uses a bitmask to ignore potential false-positives from this additional region of memory.

As we mentioned, security is our priority when it comes to optimizations, so we could never deploy something with this kind of behaviour. We ended up porting Muła’s library to Rust (we’ve also open-sourced the crate!) and performed an overlapping registers modification found in ARM’s blog.

It’s best illustrated by example — notice the difference between how we would fill registers on an imaginary SIMD instruction-set with 4-byte registers.

Before modification

Building even faster interpreters in Rust
How registers are filled in the original implementation for the haystack “abcdefghij”, red squares indicate out of bounds memory.

After modification

Building even faster interpreters in Rust
How registers are filled with the overlapping modification for the same haystack, notice how ‘g’ and ‘h’ each appear in two registers.

In our case, repeating some bytes within two different registers will never change the final outcome, so this modification is allowed as-is. However, in reality, we found it was better to use a bitmask to exclude repeated parts of the final register and minimize the number of memcmp calls.

What if the haystack is too small to even fill a single register? In this case, we can’t use our overlapping trick since there’s nothing to overlap with. Our solution is straightforward: while we were primarily targeting AVX2, which can store 32-bytes in a lane, we can easily move down to another instruction set with smaller registers that the haystack can fit into. In reality, we don’t currently go any smaller than SSE2. Beyond this, we instead use an implementation of the Rabin-Karp searching algorithm which appears to perform well.

Instruction setRegister size
AVX232 bytes
SSE216 bytes
SWAR (u64)8 bytes
SWAR (u32)4 bytes
Register sizes in different SIMD instruction sets [3]. We did not consider AVX512 since support for this is not widespread enough.

Is it always fast?

Choosing the first and last bytes of the needle to rule out potential matches is a great idea. It means that when it does come to performing a memcmp, we can ignore these, as we know they already match. Unfortunately, as Muła points out, this also makes the algorithm susceptible to a worst-case attack in some instances.

Let’s give an expression that a customer might write to illustrate this.

http.request.uri.path contains “/wp-admin/”

If we try to search for this within a very long sequence of ‘/’s, we will find a potential match in every position and make lots of calls to memcmp — essentially performing a slow bruteforce substring search.

Clearly we need to choose different bytes from the needle. But which ones should we choose? For each choice, an adversary can always find a slightly different, but equally troublesome, worst case. We instead use randomness to throw off our would-be adversary, picking the first byte of the needle as before, but then choosing another random byte to use.

Our new version is unsurprisingly slower than Muła’s, yet it still exhibits a great improvement over both the memmem and regex crates. Performance, but without sacrificing safety.

BenchmarkImprovement
sse4-strstr (original)sliceslice (our version)
Expressions using `contains` operator72.3%49.1%
‘Simple’ expressions0.0%0.1%
All expressions31.6%24.0%
Improvements in instruction count of using sse4-strstr and sliceslice over the memmem crate with Wirefilter.

What’s next?

This is only a small taste of the performance work we’ve been doing, and we have much more yet to come. Nevertheless, none of this would have been possible without the support of my manager Richard and my mentor Elie, who contributed a lot of these ideas. I’ve learned so much over the past few months, but most of all that Cloudflare is an amazing place to be an intern!

[1] Since our benchmarks are not run within a production environment, results in this post do not represent traffic on our edge.

[2] We found instruction counts to be a particularly stable measure, and CPU cycles a particularly unstable one.

[3] Note that SWAR is technically not an instruction set, but instead uses regular registers like vector registers.

Deploying defense in depth using AWS Managed Rules for AWS WAF (part 2)

Post Syndicated from Daniel Swart original https://aws.amazon.com/blogs/security/deploying-defense-in-depth-using-aws-managed-rules-for-aws-waf-part-2/

In this post, I show you how to use recent enhancements in AWS WAF to manage a multi-layer web application security enforcement policy. These enhancements will help you to maintain and deploy web application firewall configurations across deployment stages and across different types of applications.

In part 1 of this post I describe the technologies and methods that you can use to build and manage defense in depth for your network. In part 2, I will show you how to use those tools to build your defense in depth using AWS Managed Rules as the starting point and how it can be used for optimal effectiveness

Managing policies for multiple environments can be done with minimal administrative overhead and can now be part of a deployment pipeline where you programmatically enforce policies for broad edge network policy enforcements and protect production workloads without compromising on development speed or safety.

Building robust security policy enforcement relies on a layered approach and the same applies to securing your web applications. Having edge policies, application policies, and even private or internal policy enforcement layers adds to the visibility of communication requests as well as unified policy enforcement.

Using a layered AWS WAF deployment, such as is deployed by the procedure that follows, gives you greater flexibility in the amount of rules you can use and the option to standardize edge policies and production policies. This lets you test and develop new applications without comprising the production environments.

In the following example, the application load balancer is in us-east-1. To create a web ACL for Amazon CloudFront you need to deploy the stack in us-east-1. The Amazon-CloudFront-Application-Load-Balancer-AMR.yml template can create both web ACLs in this scenario.

Note: If you’re using CloudFront and hosting the origin in us-east-1, you only need to maintain one stack. If your origin is in another region, you need to deploy a stack in us-east-1 for CloudFront web ACLs and another in the region where your application load balancer is. That scenario isn’t covered in the following procedure. None of the underlying infrastructure would be deployed with the example AWS CloudFromation templates provided. Only the AWS WAF configurations would be deployed using the example templates.

Solution overview

The following diagram illustrates the traffic flow where traffic comes in via CloudFront and serves the traffic to the backend load balancers. Both CloudFront and the load balancers support AWS WAF. This is where dedicated web security policies can be enforced to build out a defense-in-depth, multi layered policy enforcement.
 

Figure 1: Defense in depth deployment on AWS WAF

Figure 1: Defense in depth deployment on AWS WAF

Creating AWS Managed Rule web ACLs

During this process we create two web ACLs that are designed for policy enforcement for two dedicated layers. The process won’t deploy the required infrastructure, such as the CloudFront distribution or application load balancers. This example template deploys a single stack in us-east-1 where the CloudFront origin load balancer is located.

To create AWS Managed Rule web ACLs

  1. Download the Amazon-CloudFront-Application-Load-Balancer-AMR.yml template.
  2. Open the AWS Management Console and select the region where the origin application load balancer is deployed. The Amazon-CloudFront-Application-Load-Balancer-AMR.yml template that you downloaded deploys both web ACLs for CloudFront and the application load balancer.
     
    Figure 2: Select a region from the console

    Figure 2: Select a region from the console

  3. Under Find Services enter AWS CloudFormation and select Enter.
     
    Figure 3: Find and select AWS CloudFormation

    Figure 3: Find and select AWS CloudFormation

  4. Select Create stack.
     
    Figure 4: Create stack

    Figure 4: Create stack

  5. Select a template file for the stack.
    1. In the Create stack window, select Template is ready and Upload a template file.
    2. Under Upload a template file, select Choose file and select the Amazon-CloudFront-Application-Load-Balancer-AMR.yml example AWS CloudFormation template you downloaded earlier.
    3. Choose Next.
    Figure 5: Prepare and choose a template

    Figure 5: Prepare and choose a template

  6. Add stack details.
    1. Enter a name for the stack in Stack name.
    2. Enter a name for the Edge Network AWS WAF WebACL and for the Public Layer AWS WAF WebACL.
    3. Set a rate-limit for HTTP GET requests in HTTP Get Flood Protection (this rate is applied per IP address over a 5 minute period).
    4. Set a rate limit for HTTP POST requests in HTTP Post Flood Protection.
    5. Use the Login URL to apply the limit to a targeted login page. If you want to rate-limit all HTTP POST requests, leave the login URL section blank.
    Figure 6: Set stack details

    Figure 6: Set stack details

  7. By default, all the rules within the rule-sets are in action override (count mode). This does not include the rate based rules. If you want to deploy selected rules in a block, remove them from the pre-populated list by highlighting and deleting them. It’s best practice to evaluate firewall rules before changing them from count to block mode. Choose Next to move to the next step.
     
    Figure 7: Default managed rules options

    Figure 7: Default managed rules options

  8. Here you can add tags to apply to the resources in the stack that these rules will be deployed to. Tagging is a recommended best practice as it enables you to add metadata information to resources during the creation. For more information on tagging please see the Tagging AWS resources documentation. Then choose Next. On the following page choose Create stack.
     
    Figure 8: Add tags

    Figure 8: Add tags

  9. Wait until the stack has been deployed. When deployment is complete, the status of the stack will change to CREATE_COMPLETE.
     
    Figure 9: Stack deployment status

    Figure 9: Stack deployment status

Associating the web ACLs to resources

During this process we associate the two newly created web ACLs to the corresponding infrastructure resources. In this example, it would be the CloudFront distribution and its origin load balancer which should have been created prior.

To associate the web ACLs to resources

  1. In the console search for and select WAF & Shield.
     
    Figure 10: Select WAF & Shield

    Figure 10: Select WAF & Shield

  2. Select Web ACLs from the list on the left.
     
    Figure 11: Select Web ACLs

    Figure 11: Select Web ACLs

  3. Select Global (CloudFront) from the drop down list at the top of the page. Choose the Edge-Network-Layer-WebACL name that you created in step 6 of the previous procedure (Creating AWS Managed Rule web ACLs).
     
    Figure 12: Select the web ACL

    Figure 12: Select the web ACL

  4. Next select Associated AWS and then choose Add AWS resources.
     
    Figure 13: Add AWS resources

    Figure 13: Add AWS resources

  5. Select the CloudFront distribution you want to protect. Choose Add.
     
    Figure 14: Select the CloudFront distribution to protect

    Figure 14: Select the CloudFront distribution to protect

  6. Select the region the application load balancer is deployed in—this example is us-east-1—and then repeat the same association process as in steps by selecting Web ACLs and now associating the Application Load Balancer similar to steps 3 and 4 above. However, this time, select the application load balancer that serves as the CloudFront Distribution origin. Select US East (N. Virginia) from the drop-down list at the top of the page. Choose the Public-Application-Layer-WebACL name that you created in step 6 of the previous procedure (Creating AWS Managed Rule web ACLs).
     
    Figure 15: Application layer Web ACL association

    Figure 15: Application layer Web ACL association

Conclusion

Using AWS WAF to manage a multi-layer web application security enforcement policy you are able to build defense in depth stack for each specific web application. The configuration will help you to maintain and deploy web application firewall configurations across deployment stages and across different types of applications. Now with AWS Managed Rules this has enabled customers to make use of prebuild rule sets that can easily be deployed to create a layered defense that will fit into customers web application deployment pipelines. For customers that would like to centrally manage and control WAF in their AWS Organization, consider AWS Firewall Manager.

The AWS CloudFormation templates used in this procedure are in this GitHub repository.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS WAF forum or contact AWS Support.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.

Author

Daniel Cisco Swart

The AWS Managed Rules was something Daniel worked on personally over a number of years during his time with the AWS Threat Research Team. Currently Daniel is working with Security competency technology partners from the AWS Partner Network as a Partner Solutions Architect enabling customer success through technical collaboration with AWS’s top security partners.

Defense in depth using AWS Managed Rules for AWS WAF (part 1)

Post Syndicated from Daniel Swart original https://aws.amazon.com/blogs/security/defense-in-depth-using-aws-managed-rules-for-aws-waf-part-1/

In this post, I discuss how you can use recent enhancements in AWS WAF to manage a multi-layer web application security enforcement policy. These enhancements will help you to maintain and deploy web application firewall configurations across deployment stages and across different types of applications.

The post is in two parts. This first part describes AWS Managed Rules for AWS WAF and how it can be used to provide defense in depth. The second part shows how to apply AWS Managed Rules for WAF.

AWS Managed Rules for AWS WAF is a service that provides groups of rules created by Amazon Web Services (AWS) or by an AWS technology partner. By using AWS Managed Rules, you can reduce the administrative overhead of configuring rules for AWS WAF. You still need a comprehensive strategy for web application policy enforcement to help you make the best use of AWS Managed Rules for your web applications.

By using a layered policy enforcement strategy, you can create policy enforcement that’s specific to each part of your applications. This helps you avoid having to maintain and manage monolithic AWS WAF configurations for each of your applications. When you can separate policies for the edge network and for the application layer network, replicating separate policies across larger workloads becomes modular. This makes your application security more agile and lets you protect public-facing web applications without writing new rules or including rules that aren’t relevant to your web application.

Policy enforcement becomes even less of an administrative burden when you use AWS Firewall Manager to enforce policies across all accounts. This helps ensure organizations have robust policy enforcement measures across multiple accounts, with increased application layer visibility.

The new AWS WAF JSON document-style configuration enables traditional code review processes. You can now easily manage AWS WAF configurations on multiple layers of your web applications. This has also enabled partners to create more dynamic and robust rules that they can deliver on AWS WAF, which ultimately helps those customers manage their web application security policies.

AWS WAF enhancements

AWS WAF uses web ACL capacity units (WCU) to calculate and control the operating resources that are used to run your rules, rule groups, and web ACLs.

You can use JSON key-value pair document-based configuration to more easily integrate AWS WAF into the development practices of your organization. As noted in the prior paragraph, using document-style configuration removes the need to use multiple API calls to create objects in the correct order before you can create and deploy a web ACL to protect your web applications.

Using this method lets firewall changes be implemented with normal development and operations best practices because it will be infrastructure as code. This enables version control and code review before deploying updates to your production environment.

Solution overview

The following diagram illustrates the layers and functions of a defense-in-depth solution. The text that follows describes each layer.
 

Figure 1: Solution overview diagram

Figure 1: Solution overview diagram

Edge network layer policy enforcement

The edge network is the first layer of policy enforcement and should be used for broad security policy enforcement. This is the ideal place for rules such as AWS Managed Rules Core rule set (CRS), geographical location blocks, IP reputational lists, anonymous IP lists, and basic rate limits enforcement. By limiting known bad traffic at the edge network, the CRS limits the exposure of the application layer to known bad IP address ranges, malicious requests, bad bots, and request floods. This provides broad protection to the inner application layer against malicious activity, which can be applied regardless of the web application being served at the application layer.

Combining Amazon CloudFront with the distributed denial of service (DDoS) mitigation capabilities of AWS Shield is supported by AWS WAF for your outer layer of web application security enforcement.

It’s a common misconception that CloudFront is only a content delivery platform, but it also has robust transparent reverse proxy capabilities. CloudFront can help protect your environment from a broad range of web application risks. For example, you can use CloudFront to ensure that HTTP requests conform to standards on the far outer layer of your web application environment while serving content closer to the user.

Application layer policy enforcement

The next level of enforcement should be an application load balancer in a public subnet with another web ACL at the CloudFront origin. This policy enforcement layer is where you create a regional web ACL for the CloudFront origin. In addition, this layer is where you apply application-specific rules. For example, if you have a web application that uses a LAMP stack, it would be best to use AWS Managed Rules for SQL Injection, Linux, and PHP as an enforcement layer.

Note: IP-based enforcement is not effective on this part of the environment. Consider making use of an origin custom header on the CloudFront distribution. Then using this custom header to create a BLOCK rule within this web ACL to deny any request without the origin custom header as the first rule in your web ACL list. This rule needs to be created manually and will not be configured by the supplied templates.

(Optional) Third-party web application firewall layer policy enforcement

AWS WAF enforces policies on inbound requests and doesn’t have outbound inspection capabilities. If you need to enforce policies based on outbound responses, you can use Amazon Machine Image (AMI) based web application firewalls, which are available via the AWS Marketplace.

Using an instance-based web application firewall is used here because most of the heavy lifting of computational expenditure is done on the AWS WAF enforcement layers. The third-party layer is where you can enforce policies that require requests to be stateful.

Using an AMI from AWS Marketplace also gives you access to capabilities such as higher visibility, threat intelligence, and robust firewall rules. This adds an additional layer of security enhancement to your environment.

(Optional) Private layer policy enforcement

When working with a traditional three-tier web architecture, you can add an additional layer of enforcement on the private layer, which can be used for the web front ends. This stage is where you would deploy an application load balancer in a private subnet serving your web front ends. This load balancer is there for any computational expensive regex-based rule enforcement that you don’t want to enforce on the instances-based WAF. This also gives you another layer of visibility before requests reaches the web front ends themselves. This example can be seen in Figure 2 below as a reference.

Use case examples

The AWS CloudFormation templates supplied can be deployed in a modular fashion. If the application load balancer is located in the us-east-1 region, you can deploy a single template called Amazon-CloudFront-Application-Load-Balancer-AMR.yml.

If the application load balancer isn’t located in us-east-1, you can use the Amazon-CloudFront-EdgeLayer-AMR.yml template to deploy the stack in us-east-1 to support the web ACL on CloudFront and then deploy ApplicationLayer-Load-Balancer-AMR.yml in the region the original application load balancer was deployed for its web ACL.

All CloudFormation templates are available on the Github project page and a summary of each can be found in the main readme.md file.

Note: All the individual rules in each rule set is set to ACTION OVERRIDE for initial deployment. If any of the rule actions in the group are set to block or allow, this override changes the behavior so that matching rules are only counted. You may change the setting to NO ACTION OVERRIDE after a period of evaluation to avoid disrupting production workloads with potential false positives.

Edge network and application load balancer origin using AWS Managed Rules for AWS WAF

When considering some of the web application best practices on AWS for resiliency and security, the recommendation is to use CloudFront where possible, because it can terminate TLS/SSL connections and serve cached content close to the end user. CloudFront has advanced mitigation capabilities such as SYN cookies and a massively distributed network separate from the traditional Amazon Elastic Compute Cloud (Amazon EC2) networking space. CloudFront also supports AWS WAF rate limits, IP blacklists, and broad security policies, which can be enforced at the edge network layer.

In the example Amazon-CloudFront-Application-Load-Balancer-AMR.yml template, we place a rate-limit for HTTP GET and HTTP POST methods. This is dependent upon expected traffic request rates. You can review Amazon CloudWatch metrics for your CloudFront distribution or application load balancer to determine the baseline for your rate limit based on the maximum expected requests per minute.

The rate limit is adjustable within the parameter options at deployment of the AWS CloudFormation template Amazon-CloudFront-Application-Load-Balancer-AMR.yml. The HTTP POST rate limit also helps to slow down credential stuffing attacks—a form of brute force attack—on login pages. The ApplicationLayer-Load-Balancer-AMR.yml template used in part 2 of this post also deploys the Amazon IP reputation list to drop IP addresses based on Amazon internal threat intelligence.

We also use the AWS Managed Rules CommonRuleSet that blocks cross-site scripting (XSS) attacks, request with no user-agents, requests with known bad user-agents, large queries, posts, cookies, and URLs, and known LFI/RFI attacks.

Note: The size constraint rules aren’t recommended for protecting APIs or web applications with large HTTP POSTs or long cookies. Evaluate the possible effects of size constraint rules thoroughly before setting them to block requests.

There is also an AWS Managed Rule for known bad inputs which is based on threat intelligence gathered by the AWS Threat Research Team. Finally, there is an admin protection rule set that drops requests to known management login pages. It’s not advised that web applications have front door access to admin controls.

At the origin, it’s a good idea to use an application load balancer that also supports AWS WAF. This is where you want to apply application-specific web policies. For example, this is where you would apply rules to protect against a SQL injection attack if your web application uses a SQL database.

In the example AWS CloudFormation template Amazon-CloudFront-Application-Load-Balancer-AMR.yml, for the origin application load balancer, we use AWS Managed Rules for SQL injections, Linux rule set, Unix rule set, PHP rule set, and the WordPress rule set to cover most eventualities customers could be using on their web applications.

For the example solution in part 2 of this post, if the origin application load balancer is in us-east-1, you can use Amazon-CloudFront-Application-Load-Balancer-AMR.yml, which will deploy both web ACLs.

If the origin is not in us-east-1, you can use two example templates which are Amazon-CloudFront-EdgeLayer-AMR.yml for the edge network and ApplicationLayer-Load-Balancer-AMR.yml in the origin region.

Using AWS Managed WAF Rules on public and private application load balancers

Some customers have reasons to not use CloudFront and will use two application load balancers. One load balancer for the public facing environment for web front ends and an internal load balancer for the application backends.

The following figure shows a deployment that uses two load balancers. A public load balancer works with the edge network WAF to connect to a web front end in a private subnet and an internal load balancer connects to the backend application.
 

Figure 2: Diagram of stacked load balancers

Figure 2: Diagram of stacked load balancers

In this use case, we can still use the same structure of edge network and application layer network, now only using load balancers. Using a three-tier web application approach to deploy web applications there will be an external facing and an internal application load balancer where you can deploy the same style of policy enforcement, but only on load balancers.

Note: To deploy something similar to this example, you can use the template EdgeLayerALB-PrivateLayerALB-AMR.yml in the relevant regions where the load balancers have been deployed.

Alarms and logging

After deploying these AWS CloudFormation templates you should consider setting CloudWatch alarms on certain metrics for the HTTP GET and HTTP POST flood rules as well as the reputation and anonymous IP lists. Customers that are familiar with developing may also opt to use Lambda responders to use CloudWatch Events to trigger and update to the rule change from COUNT to BLOCK. Also enabling full logging for each web ACL will give you higher visibility into each request and will make potential investigations easier.

Conclusion

Using the new enhancements of AWS WAF makes it easier to manage a multi-layer web application security enforcement policy by using AWS WAF to maintain and deploy web application firewall configurations across their different deployment stages, as well as across different types of applications. By making use of partner or AWS Managed Rules, administrative overhead can be significantly reduced, and with AWS Firewall Manager, customers can enforce these policies across all of an organization’s accounts. Part 2 of this post will show you one example of how this can be done.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS WAF forum or contact AWS Support.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.

Author

Daniel Cisco Swart

The AWS Managed Rules was something Daniel worked on personally over a number of years during his time with the AWS Threat Research Team. Currently Daniel is working with Security competency technology partners from the AWS Partner Network as a Partner Solutions Architect enabling customer success through technical collaboration with AWS’s top security partners.

Making the WAF 40% faster

Post Syndicated from Miguel de Moura original https://blog.cloudflare.com/making-the-waf-40-faster/

Making the WAF 40% faster

Cloudflare’s Web Application Firewall (WAF) protects against malicious attacks aiming to exploit vulnerabilities in web applications. It is continuously updated to provide comprehensive coverage against the most recent threats while ensuring a low false positive rate.

As with all Cloudflare security products, the WAF is designed to not sacrifice performance for security, but there is always room for improvement.

This blog post provides a brief overview of the latest performance improvements that were rolled out to our customers.

Transitioning from PCRE to RE2

Back in July of 2019, the WAF transitioned from using a regular expression engine based on PCRE to one inspired by RE2, which is based around using a deterministic finite automaton (DFA) instead of backtracking algorithms. This change came as a result of an outage where an update added a regular expression which backtracked enormously on certain HTTP requests, resulting in exponential execution time.

After the migration was finished, we saw no measurable difference in CPU consumption at the edge, but noticed execution time outliers in the 95th and 99th percentiles decreased, something we expected given RE2’s guarantees of a linear time execution with the size of the input.

As the WAF engine uses a thread pool, we also had to implement and tune a regex cache shared between the threads to avoid excessive memory consumption (the first implementation turned out to use a staggering amount of memory).

These changes, along with others outlined in the post-mortem blog post, helped us improve reliability and safety at the edge and have the confidence to explore further performance improvements.

But while we’ve highlighted regular expressions, they are only one of the many capabilities of the WAF engine.

Matching Stages

When an HTTP request reaches the WAF, it gets organized into several logical sections to be analyzed: method, path, headers, and body. These sections are all stored in Lua variables. If you are interested in more detail on the implementation of the WAF itself you can watch this old presentation.

Before matching these variables against specific malicious request signatures, some transformations are applied. These transformations are functions that range from simple modifications like lowercasing strings to complex tokenizers and parsers looking to fingerprint certain malicious payloads.

As the WAF currently uses a variant of the ModSecurity syntax, this is what a rule might look like:

SecRule REQUEST_BODY "@rx /\x00+evil" "drop, t:urlDecode, t:lowercase"

It takes the request body stored in the REQUEST_BODY variable, applies the urlDecode() and lowercase() functions to it and then compares the result with the regular expression signature \x00+evil.

In pseudo-code, we can represent it as:

rx( "/\x00+evil", lowercase( urlDecode( REQUEST_BODY ) ) )

Which in turn would match a request whose body contained percent encoded NULL bytes followed by the word “evil”, e.g.:

GET /cms/admin?action=post HTTP/1.1
Host: example.com
Content-Type: text/plain; charset=utf-8
Content-Length: 16

thiSis%2F%00eVil

The WAF contains thousands of these rules and its objective is to execute them as quickly as possible to minimize any added latency to a request. And to make things harder, it needs to run most of the rules on nearly every request. That’s because almost all HTTP requests are non-malicious and no rules are going to match. So we have to optimize for the worst case: execute everything!

To help mitigate this problem, one of the first matching steps executed for many rules is pre-filtering. By checking if a request contains certain bytes or sets of strings we are able to potentially skip a considerable number of expressions.

In the previous example, doing a quick check for the NULL byte (represented by \x00 in the regular expression) allows us to completely skip the rule if it isn’t found:

contains( "\x00", REQUEST_BODY )
and
rx( "/\x00+evil", lowercase( urlDecode( REQUEST_BODY ) ) )

Since most requests don’t match any rule and these checks are quick to execute, overall we aren’t doing more operations by adding them.

Other steps can also be used to scan through and combine several regular expressions and avoid execution of rule expressions. As usual, doing less work is often the simplest way to make a system faster.

Memoization

Which brings us to memoization – caching the output of a function call to reuse it in future calls.

Let’s say we have the following expressions:

1. rx( "\x00+evil", lowercase( url_decode( body ) ) )
2. rx( "\x00+EVIL", remove_spaces( url_decode( body ) ) )
3. rx( "\x00+evil", lowercase( url_decode( headers ) ) )
4. streq( "\x00evil", lowercase( url_decode( body ) ) )

In this case, we can reuse the result of the nested function calls (1) as they’re the same in (4). By saving intermediate results we are also able to take advantage of the result of url_decode( body ) from (1) and use it in (2) and (4). Sometimes it is also possible to swap the order functions are applied to improve caching, though in this case we would get different results.

A naive implementation of this system can simply be a hash table with each entry having the function(s) name(s) and arguments as the key and its output as the value.

Some of these functions are expensive and caching the result does lead to significant savings. To give a sense of magnitude, one of the rules we modified to ensure memoization took place saw its execution time reduced by about 95%:

Making the WAF 40% faster
Execution time per rule

The WAF engine implements memoization and the rules take advantage of it, but there’s always room to increase cache hits.

Rewriting Rules and Results

Cloudflare has a very regular cadence of releasing updates and new rules to the Managed Rulesets. However, as more rules are added and new functions implemented, the memoization cache hit rate tends to decrease.

To improve this, we first looked into the rules taking the most wall-clock time to execute using some of our performance metrics:

Making the WAF 40% faster
Execution time per rule

Having these, we cross-referenced them with the ones having cache misses (output is truncated with […]):

[email protected] $ ./parse.py --profile
Hit Ratio:
-------------
0.5608

Hot entries:
-------------
[urlDecode, replaceComments, REQUEST_URI, REQUEST_HEADERS, ARGS_POST]
[urlDecode, REQUEST_URI]
[urlDecode, htmlEntityDecode, jsDecode, replaceNulls, removeWhitespace, REQUEST_URI, REQUEST_HEADERS]
[urlDecode, lowercase, REQUEST_FILENAME]
[urlDecode, REQUEST_FILENAME]
[urlDecode, lowercase, replaceComments, compressWhitespace, ARGS, REQUEST_FILENAME]
[urlDecode, replaceNulls, removeWhitespace, REQUEST_URI, REQUEST_HEADERS, ARGS_POST]
[...]

Candidates:
-------------
100152A - replace t:removeWhitespace with t:compressWhitespace,t:removeWhitespace
100214 - replace t:lowercase with (?i)
100215 - replace t:lowercase with (?i)
100300 - consider REQUEST_URI over REQUEST_FILENAME
100137D - invert order of t:replaceNulls,t:lowercase
[...]

After identifying more than 40 rules, we rewrote them to take full advantage of memoization and added pre-filter checks where possible. Many of these changes were not immediately obvious, which is why we’re also creating tools to aid analysts in creating even more efficient rules. This also helps ensure they run in accordance with the latency budgets the team has set.

This change resulted in an increase of the cache hit percentage from 56% to 74%, which crucially included the most expensive transformations.

Most importantly, we also observed a sharp decrease of 40% in the average time the WAF takes to process and analyze an HTTP request at the Cloudflare edge.

Making the WAF 40% faster
WAF Request Processing – Time Average

A comparable decrease was also observed for the 95th and 99th percentiles.

Finally, we saw a drop of CPU consumption at the edge of around 4.3%.

Next Steps

While the Lua WAF has served us well throughout all these years, we are currently porting it to use the same engine powering Firewall Rules. It is based on our open-sourced wirefilter execution engine, which uses a filter syntax inspired by Wireshark®. In addition to allowing more flexible filter expressions, it provides better performance and safety.

The rule optimizations we’ve described in this blog post are not lost when moving to the new engine, however, as the changes were deliberately not specific to the current Lua engine’s implementation. And while we’re routinely profiling, benchmarking and making complex optimizations to the Firewall stack, sometimes just relatively simple changes can have a surprisingly huge effect.

Announcing AWS Managed Rules for AWS WAF

Post Syndicated from Brandon West original https://aws.amazon.com/blogs/aws/announcing-aws-managed-rules-for-aws-waf/

Building and deploying secure applications is critical work, and the threat landscape is always shifting. We’re constantly working to reduce the pain of maintaining a strong cloud security posture. Today we’re launching a new capability called AWS Managed Rules for AWS WAF that helps you protect your applications without needing to create or manage the rules directly. We’ve also made multiple improvements to AWS WAF with the launch of a new, improved console and API that makes it easier than ever to keep your applications safe.

AWS WAF is a web application firewall. It lets you define rules that give you control over which traffic to allow or deny to your application. You can use AWS WAF to help block common threats like SQL injections or cross-site scripting attacks. You can use AWS WAF with Amazon API Gateway, Amazon CloudFront, and Application Load Balancer. Today it’s getting a number of exciting improvements. Creating rules is more straightforward with the introduction of the OR operator, allowing evaluations that would previously require multiple rules. The API experience has been greatly improved, and complex rules can now be created and updated with a single API call. We’ve removed the limit of ten rules per web access control list (ACL) with the introduction of the WAF Capacity Unit (WCU). The switch to WCUs allows the creation of hundreds of rules. Each rule added to a web access control list (ACL) consumes capacity based on the type of rule being deployed, and each web ACL has a defined WCU limit.

Using the New AWS WAF

Let’s take a look at some of the changes and turn on AWS Managed Rules for AWS WAF. First, I’ll go to AWS WAF and switch over to the new version.

Next I’ll create a new web ACL and add it to an existing API Gateway resource on my account.

Now I can start adding some rules to our web ACL. With the new AWS WAF, the rules engine has been improved. Statements can be combined with AND, OR, and NOT operators, allowing for more complex rule logic.

Screenshot of the WAF v2 Boolean operators

I’m going to create a simple rule that blocks any request that uses the HTTP method POST. Another cool feature is support for multiple text transformations, so for example, you could have all your requests transformed to decode HTML entities, and then made lowercase.

JSON objects now define web ACL rules (and web ACLs themselves), making them versionable assets you can match with your application code. You can also use these JSON documents to create or update rules with a single API call.

Using AWS Managed Rules for AWS WAF

Now let’s play around with something totally new: AWS Managed Rules. AWS Managed Rules give you instant protection. The AWS Threat Research Team maintains the rules, with new ones being added as additional threats are identified. Additional rule sets are available on the AWS Marketplace. Choose a managed rule group, add it to your web ACL, and AWS WAF immediately helps protect against common threats.

I’ve selected a rule group that protects against SQL attacks, and also enabled core rule set. The core rule set covers some of the common threats and security risks described in OWASP Top 10 publication. As soon as I create the web ACL and the changes are propagated, my app will be protected from a whole range of attacks such as SQL injections. Now let’s look at both rules that I’ve added to our ACL and see how things are shaping up.

Since my demo rule was quite simple, it doesn’t require much capacity. The managed rules use a bit more, but we’ve got plenty of room to add many more rules to this web ACL.

Things to Know

That’s a quick tour of the benefits of the new and improved AWS WAF. Before you head to the console to turn it on, there’s a few things to keep in mind.

  • The new AWS WAF supports AWS CloudFormation, allowing you to create and update your web ACL and rules using CloudFormation templates.
  • There is no additional charge for using AWS Managed Rules. If you subscribe to managed rules from an AWS Marketplace seller, you will be charged the managed rules price set by the seller.
  • Pricing for AWS WAF has not changed.

As always, happy (and secure) building, and I’ll see you at re:Invent or on the re:Invent livestreams soon!

— Brandon

How to use CI/CD to deploy and configure AWS security services with Terraform

Post Syndicated from Jonathan Rau original https://aws.amazon.com/blogs/security/how-use-ci-cd-deploy-configure-aws-security-services-terraform/

Like the infrastructure your applications are built on, security infrastructure can be handled using infrastructure as code (IAC) and continuous integration/continuous deployment (CI/CD). In this post, I’ll show you how to build a CI/CD pipeline using AWS Developer Tools and HashiCorp’s Terraform platform as an IAC tool for AWS Web Application Firewall (WAF) deployments. AWS WAF is a web application firewall that helps protect your applications from common web exploits that could affect availability, compromise security, or consume excessive resources.

Terraform is an open-source tool for building, changing, and versioning infrastructure safely and efficiently. With Terraform, you can manage AWS services and custom defined provisioning logic. You create a configuration file that describes to Terraform the components needed to run a single application or your entire AWS footprint. When Terraform consumes the configuration file, it generates an execution plan describing what it will do to reach the desired state, and then executes it to build the described infrastructure.

In this solution, you’ll use Terraform configuration files to build your WAF, deploy it automatically through a CI/CD pipeline, and retain the WAF state files to be later referenced, changed, or destroyed through subsequent deployments in a durable backend. The CI/CD solution is flexible enough to deploy many other AWS services, security or otherwise, using Terraform. For a full list of supported services, see HashiCorp’s documentation.

Note: This post assumes you’re comfortable with Terraform and its core concepts, such as state management, syntax, and command terms. You can learn about Terraform here.

Solution Overview

Figure 1: Architecture diagram

Figure 1: Architecture diagram

For this solution, you’ll use AWS CodePipeline, an automated CD service to form the foundation of the CI/CD pipeline. CodePipeline helps us automate our release pipeline through build, test, and deployment. For the purpose of this post, I will not demonstrate how to configure any test or deployment stages.

The source stage uses AWS CodeCommit, which is the AWS fully-managed managed, Git-based source code management service that can be interacted with via the console and CLI. CodeCommit encrypts the source at rest and in transit, and is integrated with AWS Identity and Access Management (IAM) to customize fine-grained access controls to the source.

Note: CodePipeline supports different sources, such as S3 or GitHub – if you’re comfortable with those services, feel free to substitute them as you walk through the solution.

For the build stage, you’ll use AWS CodeBuild, which is a fully managed CI service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild uses a build specification file, which is a collection of build commands, variables and related settings, in a YAML file, that CodeBuild uses to run a build.

Finally, you’ll create a new Amazon Simple Storage Service (S3) bucket and Amazon DynamoDB table to durably store the Terraform state files outside of the CI/CD pipeline. These files are used by Terraform to map real world resources to your configuration, keep track of metadata, and to improve performance for large infrastructures.

For the purpose of this post, the security infrastructure resource deployed through the pipeline will be an AWS WAF, specifically a Global Web ACL that can attach to an Amazon CloudFront distribution, with a sample SQL Injection and Blacklist filtering rule.

The deployment steps will be as shown in Figure 1:

  1. Push artifacts, Terraform configuration files and a build specification to a CodePipeline source.
  2. CodePipeline automatically invokes CodeBuild and downloads the source files.
  3. CodeBuild installs and executes Terraform according to your build specification.
  4. Terraform stores the state files in S3 and a record of the deployment in DynamoDB.
  5. The WAF Web ACL is deployed and ready for use by your application teams.

Step 1: Set-up

In this step, you’ll create a new CodeCommit repository, S3 bucket, and DynamoDB table.

Create a CodeCommit repository

  1. Navigate to the AWS CodeCommit console, and then choose Create repository.
  2. Enter a name, description, and then choose Create. You will be taken to your repository after creation.
  3. Scroll down, and then choose Create file, as shown in Figure 2:
     
    Figure 2: CodeCommit create file

    Figure 2: CodeCommit create file

  4. You will be taken to a new screen to create a sample file, write readme into the text body, name the file readme.md, and then choose Commit changes, as shown in Figure 3:
     
    Figure 3: CodeCommit editing files

    Figure 3: CodeCommit editing files

Note: You need to create a sample file to initialize your Master branch that will not interfere with the build process. You can safely delete this file later.

Create a DynamoDB table

  1. Navigate to the Amazon DynamoDB console, and then choose Create table.
  2. Give your table a name like terraform-state-lock-dynamo.
  3. Enter LockID as your Primary key, keep the box checked for Use default settings, and then choose Create, as shown in Figure 4.

Note: Copy the name and ARN of the DynamoDB table because you will need it later when configuring your Terraform backend and CodeBuild service role.

 

Figure 4: Create DynamoDB table

Figure 4: Create DynamoDB table

Create an S3 bucket

  1. Navigate to the Amazon S3 console, and then choose Create bucket.
  2. Enter a unique name and choose the Region you have built the rest of your resources in, and then choose Next.
  3. Enable Versioning and Default encryption, and then choose Next.
  4. Select Block all public access, choose Next, and then choose Create bucket.

Note: Copy the name and ARN of the S3 bucket because you will need it later when configuring your Terraform backend and CodeBuild service role.

Step 2: Create the CI/CD pipeline

In this step, you will create the rest of your pipeline using CodePipeline and CodeBuild. If you have decided to not use CodeCommit, read CodePipeline’s documentation here about other sources.

  1. Navigate to the AWS CodePipeline console, and then choose Create pipeline.
  2. Enter a Pipeline name, select New service role, and then choose Next, as shown in Figure 5:
     
    Figure 5: CodePipeline settings

    Figure 5: CodePipeline settings

  3. Select AWS CodeCommit as the Source provider, select the name of the repository you created, and then choose master as your Branch name.
  4. Choose Amazon CloudWatch Events (recommended) as your detection option, and then choose Next, as shown in Figure 6:
     
    Figure 6: CodePipeline source stage

    Figure 6: CodePipeline source stage

  5. For Build provider, choose AWS CodeBuild and change your region as needed, and then choose Create project.

    Important: Selecting Create Project will open a new screen in your browser with the AWS CodeBuild console; do not close the browser because you will need it!

  6. Enter a Project name and description, and then scroll to the Environment section.
  7. For Environment image, choose Managed image, and then configure the following sub-selections, as shown in Figure 7:
    1. Operating system: Ubuntu
    2. Runtimes(s): Standard
    3. Image: aws/codebuild/standard:1.0
    4. Image version: Always use the latest image for this runtime version
       
      Figure 7: CodeBuild environment image

      Figure 7: CodeBuild environment image

  8. Select the checkbox under Privileged, select New service role, and take note of this Role name because you will be modifying it later.
     
    Figure 8: CodeBuild service role

    Figure 8: CodeBuild service role

  9. Choose the dropdown menu named Additional configuration (shown in Figure 8), scroll down to Environment variables, and then enter the following values, as shown in Figure 9:
    1. Name: TF_COMMAND
    2. Value: apply (this is case sensitive)
    3. Type: Plaintex
       
      Figure 9: CodeBuild variables

      Figure 9: CodeBuild variables

      Note: These values are used by the build specification to inject Terraform commands into Runtime.

  10. In the Buildspec section, choose Use a buildspec file. You don’t need to provide a name because buildspec.yaml in your ZIP package is the default value CodeBuild will look for.
  11. In the Logs section, choose the checkbox next to CloudWatch logs – optional, and then choose Continue to CodePipeline (see Figure 10).
     
    Figure 10: CodeBuild logging

    Figure 10: CodeBuild logging

    Note: The separate window will close at this point and you will be back in the CodePipeline console.

  12. Now, back in the CodePipeline console, choose Next, choose Skip deploy stage, and then choose Skip when prompted, as shown in Figure 11.
     
    Figure 11: CodePipeline skip deploy stage

    Figure 11: CodePipeline skip deploy stage

  13. Confirm your details are correct in the Review screen, and then choose Create pipeline.

After creation, you will be taken to the Pipeline Status view for the pipeline you just created. This interface allows you to monitor the status of CodePipeline in near real time. You can pivot to your Source repository and Build project by selecting the Details link, as shown in Figure 12.
 

Figure 12: CodePipeline status

Figure 12: CodePipeline status

You can also see previous CodePipeline runs by choosing the History view on the navigation pane on the left, as shown in Figure 13. This view is also useful for viewing multiple concurrent CodePipeline runs.
 

Figure 13: CodePipeline History

Figure 13: CodePipeline History

Step 3: Modify the CodeBuild service role

In this section, you will add an additional policy to your CodeBuild service role to allow Terraform to deploy your WAF and write state information to DynamoDB and S3.

  1. Navigate to the IAM Console, and then choose Roles from the navigation pane.
  2. Search for the CodeBuild service role, select it, and then choose Add inline policy.

    Note: The inline policy is used to avoid accidental deletions or modifications, and provide a one-to-one relationship between the permissions and the service role.

  3. Choose the JSON tab and paste in the following policy. Ensure you populate the Resources section of the policy with the ARN of your S3 Bucket and DynamoDB table created in Step 3.1, as shown in Figure 14.
    
    	{
        "Version": "2012-10-17",
        "Statement": [
          {
            "Sid": "WafSID",
            "Action": [
                "waf:CreateIPSet",
                "waf:CreateRule",
                "waf:CreateRuleGroup",
                "waf:CreateSqlInjectionMatchSet",
                "waf:CreateWebACL",
                "waf:DeleteIPSet",
                "waf:DeleteLoggingConfiguration",
                "waf:DeletePermissionPolicy",
                "waf:DeleteRule",
                "waf:DeleteRuleGroup",
                "waf:DeleteSqlInjectionMatchSet",
                "waf:DeleteWebACL",
                "waf:GetChangeToken",
                "waf:GetChangeTokenStatus",
                "waf:GetGeoMatchSet",
                "waf:GetIPSet",
                "waf:GetLoggingConfiguration",
                "waf:GetPermissionPolicy",
                "waf:GetRule",
                "waf:GetRuleGroup",
                "waf:GetSampledRequests",
                "waf:GetSqlInjectionMatchSet",
                "waf:GetWebACL",
                "waf:ListActivatedRulesInRuleGroup",
                "waf:ListGeoMatchSets",
                "waf:ListIPSets",
                "waf:ListLoggingConfigurations",
                "waf:ListRuleGroups",
                "waf:ListRules",
                "waf:ListSqlInjectionMatchSets",
                "waf:ListSubscribedRuleGroups",
                "waf:ListTagsForResource",
                "waf:ListWebACLs",
                "waf:PutLoggingConfiguration",
                "waf:PutPermissionPolicy",
                "waf:TagResource",
                "waf:UntagResource",
                "waf:UpdateIPSet",
                "waf:UpdateRule",
                "waf:UpdateRuleGroup",
                "waf:UpdateSqlInjectionMatchSet",
                "waf:UpdateWebACL"
              ],
            "Effect": "Allow",
            "Resource": "*"
          },
          {
            "Sid": "S3SID",
            "Action": [
              "s3:GetObject",
              "s3:ListBucket",
              "s3:PutObject"
            ],
            "Effect": "Allow",
            "Resource": ""
          },
          {
            "Sid": "DDBSID",
            "Action": [
              "dynamodb:DeleteItem",
              "dynamodb:GetItem",
              "dynamodb:PutItem"
            ],
            "Effect": "Allow",
            "Resource": ""
          }
        ]
      }
    

     

    Figure 14: IAM resource edits

    Figure 14: IAM resource edits

  4. Choose Review policy, enter a name for the inline policy, and then choose Create policy.

You now have the required permissions to deploy, modify, and delete your WAF, as needed. For pipelines that will be deploying multiple services, or using different backends for the state files, the permissions will need to be much more broadly defined.

Step 4: Deploy the WAF with CodePipeline

With all permissions and supporting infrastructure set up, you can now deploy your WAF. Navigate to this GitHub repository and clone it; there are five files you will need:

  • provider.tf
  • variables.tf
  • waf-conditions.tf
  • waf-rules.tf
  • buildspec.yaml
  1. Open the file named provider.tf in a text editor and modify the following values, as shown in Figure 15:
    1. region=: Enter your preferred AWS Region (on lines 3 & 13)
    2. bucket=: Name of your S3 bucket (on line 10)
    3. dynamodb_table=: Name of your DynamoDB table (on line 11)
       
      Figure 15: provider.tf modification

      Figure 15: provider.tf modification

  2. Save and close this file, navigate to the AWS CodeCommit console, and then select your repository.
  3. Choose the drop-down menu named Add file, and then select Upload file (see Figure 16).
     
    Figure 16: CodeCommit Upload files

    Figure 16: CodeCommit Upload files

  4. Using the Console, upload all five files downloaded from GitHub. Alternatively, you can learn how to do this using the CLI in the AWS CodeCommit User Guide.
  5. After you’ve uploaded the last file, navigate to the CodePipeline console, and then select your pipeline.

    Note: If the source message within the UI doesn’t match what you entered for your last upload commit message, use the History tab to find your execution with all files added because the previous deployments will fail due to the missing files.

  6. To access the Build project Build logs console, in the Build section, choose Details, as shown in Figure 17.
     
    Figure 17: CodePipeline status details

    Figure 17: CodePipeline status details

  7. Choose Tail logs to view logs in near real-time from the CodeBuild environment. You will be able to see the output from Terraform, as well as other information, such as errors and environmental logs, from the CodeBuild service, as shown in Figure 18.This view can be useful for debugging missing permissions for Terraform, as it will cause a failure and Terraform will log what IAM permissions were denied
     
    Figure 18: CodeBuild tail logs

    Figure 18: CodeBuild tail logs

  8. After a successful deployment, navigate to the AWS WAF Web ACL Console, and then choose the Web ACL that was deployed.
  9. Choose the Rules tab, and then select the Rules’ hyperlinks to inspect how they were created, as shown in Figure 19.
     
    Figure 19: Web ACL views

    Figure 19: Web ACL views

From here, you can associate the Global Web ACL with a CloudFront distribution to test the efficacy. This AWS Samples GitHub repository contains a more in-depth demo on how to effectively tune a WAF.

Important clean up

You will now clean up your deployed Web ACL. Doing this is important because you will be charged $5.00 USD per Web ACL, and $1.00 per rule per Web ACL, per month, on top of other related charges. Read the AWS WAF Pricing page for more details around AWS WAF pricing.

  1. Navigate to the AWS CodeBuild console, and then choose your CodeBuild project.
  2. Choose the Build details tab, scroll to the Environment section, and then choose Edit.
  3. Expand the Additional configuration drop-down menu, and then scroll to Environment variables.
  4. Under the Value of your previously created variable, replace the value with destroy, and then choose Update environment.
  5. Navigate back to the Pipelines menu in the AWS CodePipeline console, and then select your pipeline.
  6. Choose Release Change, and then choose Release, when prompted. Wait for the Build stage to report success to confirm deletion of our WAF resources.

Conclusion

In this post, you learned how to use AWS Developer Tools to create a Serverless CI/CD pipeline that you can use to automate deployments of infrastructure with Terraform. By using Terraform and CI/CD, your security engineers can deploy security infrastructure services in a clearly defined and immutable process, such as AWS WAF.

To further extend this solution, you can include manual confirmation stages via Amazon Simple Notification Service (SNS) to enforce approvals before all CI/CD pipelines deploy resources into your accounts. You can also choose to isolate your CI/CD pipelines by placing them in a VPC. Finally, you can select the WAF Rules deployed by Terraform as the starting point for a Rule group in AWS Firewall Management Service (FMS), which allows you to define multi-account WAF deployments for accounts in AWS Organizations.

Jonthan Rau

Jonathan is the Senior TPM for AWS Security Hub. He holds an AWS Certified Specialty-Security certification and is extremely passionate about cyber security, data privacy, and new emerging technologies, such as blockchain. He devotes personal time into research and advocacy about those same topics.

Cloudflare’s protection against a new Remote Code Execution vulnerability (CVE-2019-16759) in vBulletin

Post Syndicated from Alex Cruz Farmer original https://blog.cloudflare.com/cloudflares-protection-against-a-new-remote-code-execution-vulnerability-cve-2019-16759-in-vbulletin/

Cloudflare’s protection against a new Remote Code Execution vulnerability (CVE-2019-16759) in vBulletin

Cloudflare has released a new rule as part of its Cloudflare Specials Rulesets, to protect our customers against a high-severity vulnerability in vBulletin.  

A new zero-day vulnerability was discovered for vBulletin, a proprietary Internet forum software. By exploiting this vulnerability, bad actors could potentially gain privileged access and control to the host servers on which this software runs, through Remote Code Execution (RCE).

Implications of this vulnerability

At Cloudflare, we use three key indicators to understand the severity of a vulnerability 1) how many customers on Cloudflare are running the affected software 2) the Common Vulnerability Scoring System (CVSS) score, and 3) the OWASP Top 10, an open-source security framework.

We assess this vulnerability to be very significant as it has a CVSS score of 9.8/10 and affects 7 out of the 10 key risk areas of the OWASP 2017 Top 10.

Remote Code Execution is considered a type of injection, which provides the capability to potentially launch a catastrophic attack. Through RCE an attacker can gain privileged access to the host server that might be running the unpatched and vulnerable version of this software. With elevated privileges the attacker could perform malicious activities including discovery of additional vulnerabilities in the system, checks for misconfigured file permissions on configuration files and even delete logs to wipe out the possibility of audit trails to their activities.

We also have often observed attackers exploit RCE vulnerabilities to deploy malware on the host, make it part of a DDoS Botnet attack or exfiltrate valuable data stored in the system.

Cloudflare’s continuously learning Firewall has you covered

At Cloudflare, we continuously strive to improve the security posture of our customers by quickly and seamlessly mitigating vulnerabilities of this nature. Protection against common RCE attacks is a standard feature of Cloudflare’s Managed Rulesets. To provide coverage for this specific vulnerability, we have deployed a new rule within our Cloudflare Specials Rulesets (ruleId: 100166). Customers who have our Managed Rulesets and Cloudflare Specials enabled will be immediately protected against this vulnerability.

To check whether you have this protection enabled, please login, navigate to the Firewall tab and under the Managed Rulesets tab you will find the toggle to enable the WAF Managed Rulesets. See below:

Cloudflare’s protection against a new Remote Code Execution vulnerability (CVE-2019-16759) in vBulletin

Next, confirm that you have the Cloudflare Specials Rulesets enabled, by checking in the Managed Rulesets card as shown below:

Cloudflare’s protection against a new Remote Code Execution vulnerability (CVE-2019-16759) in vBulletin

Our customers who use our free services or those who don’t have Cloudflare’s Managed Rulesets turned on, can also protect themselves by deploying a patch on their own. The vBulletin team have released a security patch, the details of which can be found here.

Cloudflare’s Firewall is built on a network that continuously learns from our vast network spanning over 190 countries. In Q2’19 Cloudflare blocked an average of 44 billion cyber threats each day. Learn more about our simple, easy to use and powerful Cloudflare Firewall and protect your business today.

Supercharging Firewall Events for Self-Serve

Post Syndicated from Alex Cruz Farmer original https://blog.cloudflare.com/supercharging-firewall-events-for-self-serve/

Supercharging Firewall Events for Self-Serve

Today, I’m very pleased to announce the release of a completely overhauled version of our Firewall Event log to our Free, Pro and Business customers. This new Firewall Events log is now available in your Dashboard, and you are not required to do anything to receive this new capability.

Supercharging Firewall Events for Self-Serve

No more modals!

We have done away with those pesky modals, providing a much smoother user experience. To review more detailed information about an event, you simply click anywhere on the event list row.

Supercharging Firewall Events for Self-Serve

In the expanded view, you are provided with all the information you may need to identify or diagnose issues with your Firewall or find more details about a potential threat to your application.

Additional matches per event

Cloudflare has several Firewall features to give customers granular control of their security. With this control comes some complexity when debugging why a request was stopped by the Firewall. To help clarify what happened, we have provided an “Additional matches” count at the bottom for events triggered by multiple services or rules for the same request. Clicking the number expands a list showing each rule and service along with the corresponding action.

Supercharging Firewall Events for Self-Serve

Search for any field within a Firewall Event

This is one of my favourite parts of our new Firewall Event Log. Many of our customers have expressed their frustration with the difficulty of pinpointing specific events. This is where our new search capabilities come into their own. Customers can now filter and freeform search for any field that is visible in a Firewall Event!

Let’s say you want to find all the requests originating from a specific ISP or country where your Firewall Rules issued a JavaScript challenge. There are two different ways to do this in the UI.

Firstly, when in the detail view, you can create an include or exclude filter for that field value.

Supercharging Firewall Events for Self-Serve

Secondly, you can create a freeform filter using the “+ Add Filter” button at the top, or edit one of the already filtered fields:

Supercharging Firewall Events for Self-Serve

As illustrated above, with our WAF Managed Rules enabled in log only, we can see all the rules which would have triggered if this was a legitimate attack. This allows you to confirm that your configuration is working as expected.

Scoping your search to a specific date and time

In our old Firewall Event Log, to find an event, users had to traverse through many pages to find Events from a specific date. The last major change we have added is the capability to select a time window to view events between two points in time over the last 2 weeks. In the time selection window, Free and Pro customers can choose a 24 hour time window and our Business customers can view up to 72 hours.

Supercharging Firewall Events for Self-Serve

We want your feedback!

We need your help! Please feel free to leave any feedback on our Community forums, or open a Support ticket with any problems you find. Your feedback is critical to our product improvement process, and we look forward to hearing from you.

Stopping SharePoint’s CVE-2019-0604

Post Syndicated from Georgie Yoxall original https://blog.cloudflare.com/stopping-cve-2019-0604/

Stopping SharePoint’s CVE-2019-0604

On Saturday, 11th May 2019, we got the news of a critical web vulnerability being actively exploited in the wild by advanced persistent threats (APTs), affecting Microsoft’s SharePoint server (versions 2010 through 2019).

This was CVE-2019-0604, a Remote Code Execution vulnerability in Microsoft SharePoint Servers which was not previously known to be exploitable via the web.

Several cyber security centres including the Canadian Centre for Cyber Security and Saudi Arabia’s National Center put out alerts for this threat, indicating it was being exploited to download and execute malicious code which would in turn take complete control of servers.

The affected software versions:

  • Microsoft SharePoint Foundation 2010 Service Pack 2
  • Microsoft SharePoint Foundation 2013 Service Pack 1
  • Microsoft SharePoint Server 2010 Service Pack 2
  • Microsoft SharePoint Server 2013 Service Pack 1
  • Microsoft SharePoint Enterprise Server 2016
  • Microsoft SharePoint Server 2019

Introduction

The vulnerability was initially given a critical CVSS v3 rating of 8.8 on the Zero Day Initiative advisory (however the advisory states authentication is required). This would imply only an insider threat, someone who has authorisation within SharePoint, such as an employee, on the local network could exploit the vulnerability.

We discovered that was not always the case, since there were paths which could be reached without authentication, via external facing websites. Using the NIST NVD calculator, it determines the actual base score to be a 9.8 severity out of 10 without the authentication requirement.

As part of our internal vulnerability scoring process, we decided this was critical enough to require immediate attention. This was for a number of reasons. The first being it was a critical CVE affecting a major software ecosystem, primarily aimed at enterprise businesses. There appeared to be no stable patch available at the time. And, there were several reports of it being actively exploited in the wild by APTs.

We deployed an initial firewall rule the same day, rule 100157. This allowed us to analyse traffic and request frequency before making a decision on the default action. At the same time, it gave our customers the ability to protect their online assets from these attacks in advance of a patch.

We observed the first probes at around 4:47 PM on the 11th of May, which went on until 9:12 PM. We have reason to believe these were not successful attacks, and were simply reconnaissance probes at this point.

The online vulnerable hosts exposed to the web were largely made up of high traffic enterprise businesses, which makes sense based on the below graph from W3Techs.

Stopping SharePoint’s CVE-2019-0604
Figure 1: Depicts SharePoint’s market position (Image attributed to W3Techs)

The publicly accessible proof of concept exploit code found online did not work out of the box. Therefore it was not immediately widely used, since it required weaponisation by a more skilled adversary.

We give customers advance notice of most rule changes. However, in this case, we decided that the risk was high enough that we needed to act upon this, and so made the decision to make an immediate rule release to block this malicious traffic for all of our customers on May 13th.

We were confident enough in going default block here, as the requests we’d analysed so far did not appear to be legitimate. We took several factors into consideration to determine this, some of which are detailed below.

The bulk of requests we’d seen so far, a couple hundred, originated from cloud instances, within the same IP ranges. They were enumerating the subdomains of many websites over a short time period.

This is a fairly common scenario. Malicious actors will perform reconnaissance using various methods in an attempt to find a vulnerable host to attack, before actually exploiting the vulnerability. The query string parameters also appeared suspicious, having only the ones necessary to exploit the vulnerability and nothing more.

The rule was deployed in default block mode protecting our customers, before security researchers discovered how to weaponise the exploit and before a stable patch from Microsoft was widely adopted.

The vulnerability

Zero Day Initiative did a good job in drilling down on the root cause of this vulnerability, and how it could potentially be exploited in practice.

From debugging the .NET executable, they discovered the following functions could eventually reach the deserialisation call, and so may potentially be exploitable.

Stopping SharePoint’s CVE-2019-0604
Figure 2: Depicts the affected function calls (Image attributed to Trend Micro Zero Day Initiative)

The most interesting ones here are the “.Page_Load” and “.OnLoad” methods, as these can be directly accessed by visiting a webpage. However, only one appears to not require authentication, ItemPicker.ValidateEntity which can be reached via the Picker.aspx webpage.

The vulnerability lies in the following function calls:

EntityInstanceIdEncoder.DecodeEntityInstanceId(encodedId);
Microsoft.SharePoint.BusinessData.Infrastructure.EntityInstanceIdEncoder.DecodeEntityInstanceId(pe.Key);

Stopping SharePoint’s CVE-2019-0604
Figure 3: PickerEntity Validation (Image attributed to Trend Micro Zero Day Initiative)

The PickerEntity ValidateEntity function takes “pe” (Picker Entity) as an argument.

After checking pe.Key is not null, and it matches the necessary format: via a call to

Microsoft.SharePoint.BusinessData.Infrastucture.EntityInstanceIdEncoder.IsEncodedIdentifier(pe.Key)

it continues to define an object of identifierValues from the result of

Microsoft.SharePoint.BusinessData.Infrastructure.EntityInstanceIdEncoder.DecodeEntityInstanceId(pe.Key)

where the actual deserialisation takes place.

Otherwise, it will raise an AuthenticationException, which will display an error page to the user.

The affected function call can be seen below. First, there is a conditional check on the encodedId argument which is passed to DecodeEntityInstanceId(), if it begins with __, it will continue onto deserialising the XML Schema with xmlSerializer.Deserialize().

Stopping SharePoint’s CVE-2019-0604
Figure 4: DecodeEntityInstanceId leading to the deserialisation (Image attributed to Trend Micro Zero Day Initiative)

When reached, the encodedId (in the form of an XML serialised payload) would be deserialised, and eventually executed on the system in a SharePoint application pool context, leading to a full system compromise.

One such XML payload which spawns a calculator (calc.exe) instance via a call to command prompt (cmd.exe):

<ResourceDictionary
xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
xmlns:System="clr-namespace:System;assembly=mscorlib"
xmlns:Diag="clr-namespace:System.Diagnostics;assembly=system">
    <ObjectDataProvider x:Key="LaunchCalch" ObjectType="{x:Type Diag:Process}" MethodName="Start">
        <ObjectDataProvider.MethodParameters>
            <System:String>cmd.exe</System:String>
            <System:String>/c calc.exe</System:String>
        </ObjectDataProvider.MethodParameters>
    </ObjectDataProvider>
</ResourceDictionary>

Analysis

When we first deployed the rule in log mode, we did not initially see many hits, other than a hundred probes later that evening.

We believe this was largely due to the unknowns of the vulnerability and its exploitation, as a number of conditions had to be met to craft a working exploit that are not yet widely known.

It wasn’t until after we had set the rule in default drop mode, that we saw the attacks really start to ramp up. On Monday the 13th we observed our first exploit attempts, and on the 14th saw what we believe to be individuals manually attempting to exploit sites for this vulnerability.

Given this was a weekend, it realistically gives you 1 working day to have rolled out a patch across your organisation, before malicious actors started attempting to exploit this vulnerability.

Stopping SharePoint’s CVE-2019-0604
Figure 5: Depicts requests matched, rule 100157 was set as default block early 13th May.

Further into the week, we started seeing smaller spikes for the rule. And on the 16th May, the same day the UK’s NCSC put out an alert reporting of highly successful exploitation attempts against UK organisations, thousands of requests were dropped, primarily launched at larger enterprises and government entities.

This is often the nature of such targeted attacks, malicious actors will try to automate exploits to have the biggest possible impact, and that’s exactly what we saw here.

So far into our analysis, we’ve seen malicious hits for the following paths:

  • /_layouts/15/Picker.aspx
  • /_layouts/Picker.aspx
  • /_layouts/15/downloadexternaldata.aspx

The bulk of attacks we’ve seen have been targeting the unauthenticated Picker.aspx endpoint as one would expect, using the ItemPickerDialog type:

/_layouts/15/picker.aspx?PickerDialogType=Microsoft.SharePoint.Portal.WebControls.ItemPickerDialog

We expect the vulnerability to be exploited more when a complete exploit is publicly available, so it is important to update your systems if you have not already. We also recommend isolating these systems to the internal network in cases they do not need to be external facing, in order to avoid an unnecessary attack surface.

Sometimes it’s not practical to isolate such systems to an internal network, this is usually the case for global organisations, with teams spanning multiple locations. In these cases, we highly recommend putting these systems behind an access management solution, like Cloudflare Access. This gives you granular control over who can access resources, and has the additional benefit of auditing user access.

Microsoft initially released a patch, but it did not address all vulnerable functions, therefore customers were left vulnerable with the only options being to virtually patch their systems or shut their services down entirely until an effective fix became available.

This is a prime example of why firewalls like Cloudflare’s WAF are critical to keeping a business online. Sometimes patching is not an option, and even when it is, it can take time to roll out effectively across an enterprise.

Building an AWS Landing Zone from Scratch in Six Weeks

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/building-an-aws-landing-zone-from-scratch-in-six-weeks/

In an effort to deliver a simpler, smarter, and more unified experience on its website, the UK’s Ministry of Justice and its Lead Technical Architect, James Abley, created a bespoke AWS Landing Zone, a pre-defined template for an AWS account or infrastructure. And they did it in six weeks.

Supporting 33 agencies and public bodies, and making sure they all work together, the Ministry of Justice is at the heart of the United Kingdom’s justice system. Its task is to look after all parts of the justice system, including the courts, prisons, probation services, and legal aid, striving to bring the principles of justice to life for everyone in society.

In a This Is My Architecture video, shot at 2018 re:Invent in Las Vegas, James talks with AWS Solutions Architect, Simon Treacy, about the importance of delivering a consistent experience to his website’s customers, a mix of citizen and internal legal aid agency case workers.

Utilizing a number of AWS services, James walks us through the user experience, and he why decided to put AWS CoudFront and AWS Web Application Firewall (WAF) up front to improve the security posture of the ministry’s legacy applications and extend their lifespan. James also explained how he split traffic between two availability zones, using AWS Elastic Load Balancing (ELB) to provide higher availability and resilience, which will help with zero downtime deployment later on.

 

About the author

Annik StahlAnnik Stahl is a Senior Program Manager in AWS, specializing in blog and magazine content as well as customer ratings and satisfaction. Having been the face of Microsoft Office for 10 years as the Crabby Office Lady columnist, she loves getting to know her customers and wants to hear from you.

 

This Is My Architecture: Mobile Cryptocurrency Mining

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/this-is-my-architecture-mobile-cryptocurrency-mining/

In North America, approximately 95% of adults over the age of 25 have a bank account. In the developing world, that number is only about 52%. Cryptocurrencies can provide a platform for millions of unbanked people in the world to achieve financial freedom on a more level financial playing field.

Electroneum, a cryptocurrency company located in England, built its cryptocurrency mobile back end on AWS and is using the power of blockchain to unlock the global digital economy for millions of people in the developing world.

Electroneum’s cryptocurrency mobile app allows Electroneum customers in developing countries to transfer ETNs (exchange-traded notes) and pay for goods using their smartphones. Listen in to the discussion between AWS Solutions Architect Toby Knight and Electroneum CTO Barry Last as they explain how the company built its solution. Electroneum’s app is a web application that uses a feedback loop between its web servers and AWS WAF (a web application firewall) to automatically block malicious actors. The system then uses Athena, with a gamified approach, to provide an additional layer of blocking to prevent DDoS attacks. Finally, Electroneum built a serverless, instant payments system using AWS API Gateway, AWS Lambda, and Amazon DynamoDB to help its customers avoid the usual delays in confirming cryptocurrency transactions.

 

Protecting your API using Amazon API Gateway and AWS WAF — Part I

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/protecting-your-api-using-amazon-api-gateway-and-aws-waf-part-i/

This post courtesy of Thiago Morais, AWS Solutions Architect

When you build web applications or expose any data externally, you probably look for a platform where you can build highly scalable, secure, and robust REST APIs. As APIs are publicly exposed, there are a number of best practices for providing a secure mechanism to consumers using your API.

Amazon API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management.

In this post, I show you how to take advantage of the regional API endpoint feature in API Gateway, so that you can create your own Amazon CloudFront distribution and secure your API using AWS WAF.

AWS WAF is a web application firewall that helps protect your web applications from common web exploits that could affect application availability, compromise security, or consume excessive resources.

As you make your APIs publicly available, you are exposed to attackers trying to exploit your services in several ways. The AWS security team published a whitepaper solution using AWS WAF, How to Mitigate OWASP’s Top 10 Web Application Vulnerabilities.

Regional API endpoints

Edge-optimized APIs are endpoints that are accessed through a CloudFront distribution created and managed by API Gateway. Before the launch of regional API endpoints, this was the default option when creating APIs using API Gateway. It primarily helped to reduce latency for API consumers that were located in different geographical locations than your API.

When API requests predominantly originate from an Amazon EC2 instance or other services within the same AWS Region as the API is deployed, a regional API endpoint typically lowers the latency of connections. It is recommended for such scenarios.

For better control around caching strategies, customers can use their own CloudFront distribution for regional APIs. They also have the ability to use AWS WAF protection, as I describe in this post.

Edge-optimized API endpoint

The following diagram is an illustrated example of the edge-optimized API endpoint where your API clients access your API through a CloudFront distribution created and managed by API Gateway.

Regional API endpoint

For the regional API endpoint, your customers access your API from the same Region in which your REST API is deployed. This helps you to reduce request latency and particularly allows you to add your own content delivery network, as needed.

Walkthrough

In this section, you implement the following steps:

  • Create a regional API using the PetStore sample API.
  • Create a CloudFront distribution for the API.
  • Test the CloudFront distribution.
  • Set up AWS WAF and create a web ACL.
  • Attach the web ACL to the CloudFront distribution.
  • Test AWS WAF protection.

Create the regional API

For this walkthrough, use an existing PetStore API. All new APIs launch by default as the regional endpoint type. To change the endpoint type for your existing API, choose the cog icon on the top right corner:

After you have created the PetStore API on your account, deploy a stage called “prod” for the PetStore API.

On the API Gateway console, select the PetStore API and choose Actions, Deploy API.

For Stage name, type prod and add a stage description.

Choose Deploy and the new API stage is created.

Use the following AWS CLI command to update your API from edge-optimized to regional:

aws apigateway update-rest-api \
--rest-api-id {rest-api-id} \
--patch-operations op=replace,path=/endpointConfiguration/types/EDGE,value=REGIONAL

A successful response looks like the following:

{
    "description": "Your first API with Amazon API Gateway. This is a sample API that integrates via HTTP with your demo Pet Store endpoints", 
    "createdDate": 1511525626, 
    "endpointConfiguration": {
        "types": [
            "REGIONAL"
        ]
    }, 
    "id": "{api-id}", 
    "name": "PetStore"
}

After you change your API endpoint to regional, you can now assign your own CloudFront distribution to this API.

Create a CloudFront distribution

To make things easier, I have provided an AWS CloudFormation template to deploy a CloudFront distribution pointing to the API that you just created. Click the button to deploy the template in the us-east-1 Region.

For Stack name, enter RegionalAPI. For APIGWEndpoint, enter your API FQDN in the following format:

{api-id}.execute-api.us-east-1.amazonaws.com

After you fill out the parameters, choose Next to continue the stack deployment. It takes a couple of minutes to finish the deployment. After it finishes, the Output tab lists the following items:

  • A CloudFront domain URL
  • An S3 bucket for CloudFront access logs
Output from CloudFormation

Output from CloudFormation

Test the CloudFront distribution

To see if the CloudFront distribution was configured correctly, use a web browser and enter the URL from your distribution, with the following parameters:

https://{your-distribution-url}.cloudfront.net/{api-stage}/pets

You should get the following output:

[
  {
    "id": 1,
    "type": "dog",
    "price": 249.99
  },
  {
    "id": 2,
    "type": "cat",
    "price": 124.99
  },
  {
    "id": 3,
    "type": "fish",
    "price": 0.99
  }
]

Set up AWS WAF and create a web ACL

With the new CloudFront distribution in place, you can now start setting up AWS WAF to protect your API.

For this demo, you deploy the AWS WAF Security Automations solution, which provides fine-grained control over the requests attempting to access your API.

For more information about deployment, see Automated Deployment. If you prefer, you can launch the solution directly into your account using the following button.

For CloudFront Access Log Bucket Name, add the name of the bucket created during the deployment of the CloudFormation stack for your CloudFront distribution.

The solution allows you to adjust thresholds and also choose which automations to enable to protect your API. After you finish configuring these settings, choose Next.

To start the deployment process in your account, follow the creation wizard and choose Create. It takes a few minutes do finish the deployment. You can follow the creation process through the CloudFormation console.

After the deployment finishes, you can see the new web ACL deployed on the AWS WAF console, AWSWAFSecurityAutomations.

Attach the AWS WAF web ACL to the CloudFront distribution

With the solution deployed, you can now attach the AWS WAF web ACL to the CloudFront distribution that you created earlier.

To assign the newly created AWS WAF web ACL, go back to your CloudFront distribution. After you open your distribution for editing, choose General, Edit.

Select the new AWS WAF web ACL that you created earlier, AWSWAFSecurityAutomations.

Save the changes to your CloudFront distribution and wait for the deployment to finish.

Test AWS WAF protection

To validate the AWS WAF Web ACL setup, use Artillery to load test your API and see AWS WAF in action.

To install Artillery on your machine, run the following command:

$ npm install -g artillery

After the installation completes, you can check if Artillery installed successfully by running the following command:

$ artillery -V
$ 1.6.0-12

As the time of publication, Artillery is on version 1.6.0-12.

One of the WAF web ACL rules that you have set up is a rate-based rule. By default, it is set up to block any requesters that exceed 2000 requests under 5 minutes. Try this out.

First, use cURL to query your distribution and see the API output:

$ curl -s https://{distribution-name}.cloudfront.net/prod/pets
[
  {
    "id": 1,
    "type": "dog",
    "price": 249.99
  },
  {
    "id": 2,
    "type": "cat",
    "price": 124.99
  },
  {
    "id": 3,
    "type": "fish",
    "price": 0.99
  }
]

Based on the test above, the result looks good. But what if you max out the 2000 requests in under 5 minutes?

Run the following Artillery command:

artillery quick -n 2000 --count 10  https://{distribution-name}.cloudfront.net/prod/pets

What you are doing is firing 2000 requests to your API from 10 concurrent users. For brevity, I am not posting the Artillery output here.

After Artillery finishes its execution, try to run the cURL request again and see what happens:

 

$ curl -s https://{distribution-name}.cloudfront.net/prod/pets

<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<HTML><HEAD><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=iso-8859-1">
<TITLE>ERROR: The request could not be satisfied</TITLE>
</HEAD><BODY>
<H1>ERROR</H1>
<H2>The request could not be satisfied.</H2>
<HR noshade size="1px">
Request blocked.
<BR clear="all">
<HR noshade size="1px">
<PRE>
Generated by cloudfront (CloudFront)
Request ID: [removed]
</PRE>
<ADDRESS>
</ADDRESS>
</BODY></HTML>

As you can see from the output above, the request was blocked by AWS WAF. Your IP address is removed from the blocked list after it falls below the request limit rate.

Conclusion

In this first part, you saw how to use the new API Gateway regional API endpoint together with Amazon CloudFront and AWS WAF to secure your API from a series of attacks.

In the second part, I will demonstrate some other techniques to protect your API using API keys and Amazon CloudFront custom headers.

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PTData Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PTData Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PTAccelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PTHow to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT.NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PTHybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PTUsing AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PTUsing Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

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May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

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May 31, 2018 | 09:00 AM – 09:45 AM PTMaking Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

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May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

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A geometric Rust adventure

Post Syndicated from Eevee original https://eev.ee/blog/2018/03/30/a-geometric-rust-adventure/

Hi. Yes. Sorry. I’ve been trying to write this post for ages, but I’ve also been working on a huge writing project, and apparently I have a very limited amount of writing mana at my disposal. I think this is supposed to be a Patreon reward from January. My bad. I hope it’s super great to make up for the wait!

I recently ported some math code from C++ to Rust in an attempt to do a cool thing with Doom. Here is my story.

The problem

I presented it recently as a conundrum (spoilers: I solved it!), but most of those details are unimportant.

The short version is: I have some shapes. I want to find their intersection.

Really, I want more than that: I want to drop them all on a canvas, intersect everything with everything, and pluck out all the resulting polygons. The input is a set of cookie cutters, and I want to press them all down on the same sheet of dough and figure out what all the resulting contiguous pieces are. And I want to know which cookie cutter(s) each piece came from.

But intersection is a good start.

Example of the goal.  Given two squares that overlap at their corners, I want to find the small overlap piece, plus the two L-shaped pieces left over from each square

I’m carefully referring to the input as shapes rather than polygons, because each one could be a completely arbitrary collection of lines. Obviously there’s not much you can do with shapes that aren’t even closed, but at the very least, I need to handle concavity and multiple disconnected polygons that together are considered a single input.

This is a non-trivial problem with a lot of edge cases, and offhand I don’t know how to solve it robustly. I’m not too eager to go figure it out from scratch, so I went hunting for something I could build from.

(Infuriatingly enough, I can just dump all the shapes out in an SVG file and any SVG viewer can immediately solve the problem, but that doesn’t quite help me. Though I have had a few people suggest I just rasterize the whole damn problem, and after all this, I’m starting to think they may have a point.)

Alas, I couldn’t find a Rust library for doing this. I had a hard time finding any library for doing this that wasn’t a massive fully-featured geometry engine. (I could’ve used that, but I wanted to avoid non-Rust dependencies if possible, since distributing software is already enough of a nightmare.)

A Twitter follower directed me towards a paper that described how to do very nearly what I wanted and nothing else: “A simple algorithm for Boolean operations on polygons” by F. Martínez (2013). Being an academic paper, it’s trapped in paywall hell; sorry about that. (And as I understand it, none of the money you’d pay to get the paper would even go to the authors? Is that right? What a horrible and predatory system for discovering and disseminating knowledge.)

The paper isn’t especially long, but it does describe an awful lot of subtle details and is mostly written in terms of its own reference implementation. Rather than write my own implementation based solely on the paper, I decided to try porting the reference implementation from C++ to Rust.

And so I fell down the rabbit hole.

The basic algorithm

Thankfully, the author has published the sample code on his own website, if you want to follow along. (It’s the bottom link; the same author has, confusingly, published two papers on the same topic with similar titles, four years apart.)

If not, let me describe the algorithm and how the code is generally laid out. The algorithm itself is based on a sweep line, where a vertical line passes across the plane and ✨ does stuff ✨ as it encounters various objects. This implementation has no physical line; instead, it keeps track of which segments from the original polygon would be intersecting the sweep line, which is all we really care about.

A vertical line is passing rightwards over a couple intersecting shapes.  The line current intersects two of the shapes' sides, and these two sides are the "sweep list"

The code is all bundled inside a class with only a single public method, run, because… that’s… more object-oriented, I guess. There are several helper methods, and state is stored in some attributes. A rough outline of run is:

  1. Run through all the line segments in both input polygons. For each one, generate two SweepEvents (one for each endpoint) and add them to a std::deque for storage.

    Add pointers to the two SweepEvents to a std::priority_queue, the event queue. This queue uses a custom comparator to order the events from left to right, so the top element is always the leftmost endpoint.

  2. Loop over the event queue (where an “event” means the sweep line passed over the left or right end of a segment). Encountering a left endpoint means the sweep line is newly touching that segment, so add it to a std::set called the sweep list. An important point is that std::set is ordered, and the sweep list uses a comparator that keeps segments in order vertically.

    Encountering a right endpoint means the sweep line is leaving a segment, so that segment is removed from the sweep list.

  3. When a segment is added to the sweep list, it may have up to two neighbors: the segment above it and the segment below it. Call possibleIntersection to check whether it intersects either of those neighbors. (This is nearly sufficient to find all intersections, which is neat.)

  4. If possibleIntersection detects an intersection, it will split each segment into two pieces then and there. The old segment is shortened in-place to become the left part, and a new segment is created for the right part. The new endpoints at the point of intersection are added to the event queue.

  5. Some bookkeeping is done along the way to track which original polygons each segment is inside, and eventually the segments are reconstructed into new polygons.

Hopefully that’s enough to follow along. It took me an inordinately long time to tease this out. The comments aren’t especially helpful.

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    std::deque<SweepEvent> eventHolder;    // It holds the events generated during the computation of the boolean operation

Syntax and basic semantics

The first step was to get something that rustc could at least parse, which meant translating C++ syntax to Rust syntax.

This was surprisingly straightforward! C++ classes become Rust structs. (There was no inheritance here, thankfully.) All the method declarations go away. Method implementations only need to be indented and wrapped in impl.

I did encounter some unnecessarily obtuse uses of the ternary operator:

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(prevprev != sl.begin()) ? --prevprev : prevprev = sl.end();

Rust doesn’t have a ternary — you can use a regular if block as an expression — so I expanded these out.

C++ switch blocks become Rust match blocks, but otherwise function basically the same. Rust’s enums are scoped (hallelujah), so I had to explicitly spell out where enum values came from.

The only really annoying part was changing function signatures; C++ types don’t look much at all like Rust types, save for the use of angle brackets. Rust also doesn’t pass by implicit reference, so I needed to sprinkle a few &s around.

I would’ve had a much harder time here if this code had relied on any remotely esoteric C++ functionality, but thankfully it stuck to pretty vanilla features.

Language conventions

This is a geometry problem, so the sample code unsurprisingly has its own home-grown point type. Rather than port that type to Rust, I opted to use the popular euclid crate. Not only is it code I didn’t have to write, but it already does several things that the C++ code was doing by hand inline, like dot products and cross products. And all I had to do was add one line to Cargo.toml to use it! I have no idea how anyone writes C or C++ without a package manager.

The C++ code used getters, i.e. point.x (). I’m not a huge fan of getters, though I do still appreciate the need for them in lowish-level systems languages where you want to future-proof your API and the language wants to keep a clear distinction between attribute access and method calls. But this is a point, which is nothing more than two of the same numeric type glued together; what possible future logic might you add to an accessor? The euclid authors appear to side with me and leave the coordinates as public fields, so I took great joy in removing all the superfluous parentheses.

Polygons are represented with a Polygon class, which has some number of Contours. A contour is a single contiguous loop. Something you’d usually think of as a polygon would only have one, but a shape with a hole would have two: one for the outside, one for the inside. The weird part of this arrangement was that Polygon implemented nearly the entire STL container interface, then waffled between using it and not using it throughout the rest of the code. Rust lets anything in the same module access non-public fields, so I just skipped all that and used polygon.contours directly. Hell, I think I made contours public.

Finally, the SweepEvent type has a pol field that’s declared as an enum PolygonType (either SUBJECT or CLIPPING, to indicate which of the two inputs it is), but then some other code uses the same field as a numeric index into a polygon’s contours. Boy I sure do love static typing where everything’s a goddamn integer. I wanted to extend the algorithm to work on arbitrarily many input polygons anyway, so I scrapped the enum and this became a usize.


Then I got to all the uses of STL. I have only a passing familiarity with the C++ standard library, and this code actually made modest use of it, which caused some fun days-long misunderstandings.

As mentioned, the SweepEvents are stored in a std::deque, which is never read from. It took me a little thinking to realize that the deque was being used as an arena: it’s the canonical home for the structs so pointers to them can be tossed around freely. (It can’t be a std::vector, because that could reallocate and invalidate all the pointers; std::deque is probably a doubly-linked list, and guarantees no reallocation.)

Rust’s standard library does have a doubly-linked list type, but I knew I’d run into ownership hell here later anyway, so I think I replaced it with a Rust Vec to start with. It won’t compile either way, so whatever. We’ll get back to this in a moment.

The list of segments currently intersecting the sweep line is stored in a std::set. That type is explicitly ordered, which I’m very glad I knew already. Rust has two set types, HashSet and BTreeSet; unsurprisingly, the former is unordered and the latter is ordered. Dropping in BTreeSet and fixing some method names got me 90% of the way there.

Which brought me to the other 90%. See, the C++ code also relies on finding nodes adjacent to the node that was just inserted, via STL iterators.

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next = prev = se->posSL = it = sl.insert(se).first;
(prev != sl.begin()) ? --prev : prev = sl.end();
++next;

I freely admit I’m bad at C++, but this seems like something that could’ve used… I don’t know, 1 comment. Or variable names more than two letters long. What it actually does is:

  1. Add the current sweep event (se) to the sweep list (sl), which returns a pair whose first element is an iterator pointing at the just-inserted event.

  2. Copies that iterator to several other variables, including prev and next.

  3. If the event was inserted at the beginning of the sweep list, set prev to the sweep list’s end iterator, which in C++ is a legal-but-invalid iterator meaning “the space after the end” or something. This is checked for in later code, to see if there is a previous event to look at. Otherwise, decrement prev, so it’s now pointing at the event immediately before the inserted one.

  4. Increment next normally. If the inserted event is last, then this will bump next to the end iterator anyway.

In other words, I need to get the previous and next elements from a BTreeSet. Rust does have bidirectional iterators, which BTreeSet supports… but BTreeSet::insert only returns a bool telling me whether or not anything was inserted, not the position. I came up with this:

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let mut maybe_below = active_segments.range(..segment).last().map(|v| *v);
let mut maybe_above = active_segments.range(segment..).next().map(|v| *v);
active_segments.insert(segment);

The range method returns an iterator over a subset of the tree. The .. syntax makes a range (where the right endpoint is exclusive), so ..segment finds the part of the tree before the new segment, and segment.. finds the part of the tree after it. (The latter would start with the segment itself, except I haven’t inserted it yet, so it’s not actually there.)

Then the standard next() and last() methods on bidirectional iterators find me the element I actually want. But the iterator might be empty, so they both return an Option. Also, iterators tend to return references to their contents, but in this case the contents are already references, and I don’t want a double reference, so the map call dereferences one layer — but only if the Option contains a value. Phew!

This is slightly less efficient than the C++ code, since it has to look up where segment goes three times rather than just one. I might be able to get it down to two with some more clever finagling of the iterator, but microsopic performance considerations were a low priority here.

Finally, the event queue uses a std::priority_queue to keep events in a desired order and efficiently pop the next one off the top.

Except priority queues act like heaps, where the greatest (i.e., last) item is made accessible.

Sorting out sorting

C++ comparison functions return true to indicate that the first argument is less than the second argument. Sweep events occur from left to right. You generally implement sorts so that the first thing comes, erm, first.

But sweep events go in a priority queue, and priority queues surface the last item, not the first. This C++ code handled this minor wrinkle by implementing its comparison backwards.

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struct SweepEventComp : public std::binary_function<SweepEvent, SweepEvent, bool> { // for sorting sweep events
// Compare two sweep events
// Return true means that e1 is placed at the event queue after e2, i.e,, e1 is processed by the algorithm after e2
bool operator() (const SweepEvent* e1, const SweepEvent* e2)
{
    if (e1->point.x () > e2->point.x ()) // Different x-coordinate
        return true;
    if (e2->point.x () > e1->point.x ()) // Different x-coordinate
        return false;
    if (e1->point.y () != e2->point.y ()) // Different points, but same x-coordinate. The event with lower y-coordinate is processed first
        return e1->point.y () > e2->point.y ();
    if (e1->left != e2->left) // Same point, but one is a left endpoint and the other a right endpoint. The right endpoint is processed first
        return e1->left;
    // Same point, both events are left endpoints or both are right endpoints.
    if (signedArea (e1->point, e1->otherEvent->point, e2->otherEvent->point) != 0) // not collinear
        return e1->above (e2->otherEvent->point); // the event associate to the bottom segment is processed first
    return e1->pol > e2->pol;
}
};

Maybe it’s just me, but I had a hell of a time just figuring out what problem this was even trying to solve. I still have to reread it several times whenever I look at it, to make sure I’m getting the right things backwards.

Making this even more ridiculous is that there’s a second implementation of this same sort, with the same name, in another file — and that one’s implemented forwards. And doesn’t use a tiebreaker. I don’t entirely understand how this even compiles, but it does!

I painstakingly translated this forwards to Rust. Unlike the STL, Rust doesn’t take custom comparators for its containers, so I had to implement ordering on the types themselves (which makes sense, anyway). I wrapped everything in the priority queue in a Reverse, which does what it sounds like.

I’m fairly pleased with Rust’s ordering model. Most of the work is done in Ord, a trait with a cmp() method returning an Ordering (one of Less, Equal, and Greater). No magic numbers, no need to implement all six ordering methods! It’s incredible. Ordering even has some handy methods on it, so the usual case of “order by this, then by this” can be written as:

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return self.point().x.cmp(&other.point().x)
    .then(self.point().y.cmp(&other.point().y));

Well. Just kidding! It’s not quite that easy. You see, the points here are composed of floats, and floats have the fun property that not all of them are comparable. Specifically, NaN is not less than, greater than, or equal to anything else, including itself. So IEEE 754 float ordering cannot be expressed with Ord. Unless you want to just make up an answer for NaN, but Rust doesn’t tend to do that.

Rust’s float types thus implement the weaker PartialOrd, whose method returns an Option<Ordering> instead. That makes the above example slightly uglier:

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return self.point().x.partial_cmp(&other.point().x).unwrap()
    .then(self.point().y.partial_cmp(&other.point().y).unwrap())

Also, since I use unwrap() here, this code will panic and take the whole program down if the points are infinite or NaN. Don’t do that.

This caused some minor inconveniences in other places; for example, the general-purpose cmp::min() doesn’t work on floats, because it requires an Ord-erable type. Thankfully there’s a f64::min(), which handles a NaN by returning the other argument.

(Cool story: for the longest time I had this code using f32s. I’m used to translating int to “32 bits”, and apparently that instinct kicked in for floats as well, even floats spelled double.)

The only other sorting adventure was this:

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// Due to overlapping edges the resultEvents array can be not wholly sorted
bool sorted = false;
while (!sorted) {
    sorted = true;
    for (unsigned int i = 0; i < resultEvents.size (); ++i) {
        if (i + 1 < resultEvents.size () && sec (resultEvents[i], resultEvents[i+1])) {
            std::swap (resultEvents[i], resultEvents[i+1]);
            sorted = false;
        }
    }
}

(I originally misread this comment as saying “the array cannot be wholly sorted” and had no idea why that would be the case, or why the author would then immediately attempt to bubble sort it.)

I’m still not sure why this uses an ad-hoc sort instead of std::sort. But I’m used to taking for granted that general-purpose sorting implementations are tuned to work well for almost-sorted data, like Python’s. Maybe C++ is untrustworthy here, for some reason. I replaced it with a call to .sort() and all seemed fine.

Phew! We’re getting there. Finally, my code appears to type-check.

But now I see storm clouds gathering on the horizon.

Ownership hell

I have a problem. I somehow run into this problem every single time I use Rust. The solutions are never especially satisfying, and all the hacks I might use if forced to write C++ turn out to be unsound, which is even more annoying because rustc is just sitting there with this smug “I told you so expression” and—

The problem is ownership, which Rust is fundamentally built on. Any given value must have exactly one owner, and Rust must be able to statically convince itself that:

  1. No reference to a value outlives that value.
  2. If a mutable reference to a value exists, no other references to that value exist at the same time.

This is the core of Rust. It guarantees at compile time that you cannot lose pointers to allocated memory, you cannot double-free, you cannot have dangling pointers.

It also completely thwarts a lot of approaches you might be inclined to take if you come from managed languages (where who cares, the GC will take care of it) or C++ (where you just throw pointers everywhere and hope for the best apparently).

For example, pointer loops are impossible. Rust’s understanding of ownership and lifetimes is hierarchical, and it simply cannot express loops. (Rust’s own doubly-linked list type uses raw pointers and unsafe code under the hood, where “unsafe” is an escape hatch for the usual ownership rules. Since I only recently realized that pointers to the inside of a mutable Vec are a bad idea, I figure I should probably not be writing unsafe code myself.)

This throws a few wrenches in the works.

Problem the first: pointer loops

I immediately ran into trouble with the SweepEvent struct itself. A SweepEvent pulls double duty: it represents one endpoint of a segment, but each left endpoint also handles bookkeeping for the segment itself — which means that most of the fields on a right endpoint are unused. Also, and more importantly, each SweepEvent has a pointer to the corresponding SweepEvent at the other end of the same segment. So a pair of SweepEvents point to each other.

Rust frowns upon this. In retrospect, I think I could’ve kept it working, but I also think I’m wrong about that.

My first step was to wrench SweepEvent apart. I moved all of the segment-stuff (which is virtually all of it) into a single SweepSegment type, and then populated the event queue with a SweepEndpoint tuple struct, similar to:

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enum SegmentEnd {
    Left,
    Right,
}

struct SweepEndpoint<'a>(&'a SweepSegment, SegmentEnd);

This makes SweepEndpoint essentially a tuple with a name. The 'a is a lifetime and says, more or less, that a SweepEndpoint cannot outlive the SweepSegment it references. Makes sense.

Problem solved! I no longer have mutually referential pointers. But I do still have pointers (well, references), and they have to point to something.

Problem the second: where’s all the data

Which brings me to the problem I always run into with Rust. I have a bucket of things, and I need to refer to some of them multiple times.

I tried half a dozen different approaches here and don’t clearly remember all of them, but I think my core problem went as follows. I translated the C++ class to a Rust struct with some methods hanging off of it. A simplified version might look like this.

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struct Algorithm {
    arena: LinkedList<SweepSegment>,
    event_queue: BinaryHeap<SweepEndpoint>,
}

Ah, hang on — SweepEndpoint needs to be annotated with a lifetime, so Rust can enforce that those endpoints don’t live longer than the segments they refer to. No problem?

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struct Algorithm<'a> {
    arena: LinkedList<SweepSegment>,
    event_queue: BinaryHeap<SweepEndpoint<'a>>,
}

Okay! Now for some methods.

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fn run(&mut self) {
    self.arena.push_back(SweepSegment{ data: 5 });
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Left));
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Right));
    for event in &self.event_queue {
        println!("{:?}", event)
    }
}

Aaand… this doesn’t work. Rust “cannot infer an appropriate lifetime for autoref due to conflicting requirements”. The trouble is that self.arena.back() takes a reference to self.arena, and then I put that reference in the event queue. But I promised that everything in the event queue has lifetime 'a, and I don’t actually know how long self lives here; I only know that it can’t outlive 'a, because that would invalidate the references it holds.

A little random guessing let me to change &mut self to &'a mut self — which is fine because the entire impl block this lives in is already parameterized by 'a — and that makes this compile! Hooray! I think that’s because I’m saying self itself has exactly the same lifetime as the references it holds onto, which is true, since it’s referring to itself.

Let’s get a little more ambitious and try having two segments.

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fn run(&'a mut self) {
    self.arena.push_back(SweepSegment{ data: 5 });
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Left));
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Right));
    self.arena.push_back(SweepSegment{ data: 17 });
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Left));
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Right));
    for event in &self.event_queue {
        println!("{:?}", event)
    }
}

Whoops! Rust complains that I’m trying to mutate self.arena while other stuff is referring to it. And, yes, that’s true — I have references to it in the event queue, and Rust is preventing me from potentially deleting everything from the queue when references to it still exist. I’m not actually deleting anything here, of course (though I could be if this were a Vec!), but Rust’s type system can’t encode that (and I dread the thought of a type system that can).

I struggled with this for a while, and rapidly encountered another complete showstopper:

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fn run(&'a mut self) {
    self.mutate_something();
    self.mutate_something();
}

fn mutate_something(&'a mut self) {}

Rust objects that I’m trying to borrow self mutably, twice — once for the first call, once for the second.

But why? A borrow is supposed to end automatically once it’s no longer used, right? Maybe if I throw some braces around it for scope… nope, that doesn’t help either.

It’s true that borrows usually end automatically, but here I have explicitly told Rust that mutate_something() should borrow with the lifetime 'a, which is the same as the lifetime in run(). So the first call explicitly borrows self for at least the rest of the method. Removing the lifetime from mutate_something() does fix this error, but if that method tries to add new segments, I’m back to the original problem.

Oh no. The mutation in the C++ code is several calls deep. Porting it directly seems nearly impossible.

The typical solution here — at least, the first thing people suggest to me on Twitter — is to wrap basically everything everywhere in Rc<RefCell<T>>, which gives you something that’s reference-counted (avoiding questions of ownership) and defers borrow checks until runtime (avoiding questions of mutable borrows). But that seems pretty heavy-handed here — not only does RefCell add .borrow() noise anywhere you actually want to interact with the underlying value, but do I really need to refcount these tiny structs that only hold a handful of floats each?

I set out to find a middle ground.

Solution, kind of

I really, really didn’t want to perform serious surgery on this code just to get it to build. I still didn’t know if it worked at all, and now I had to rearrange it without being able to check if I was breaking it further. (This isn’t Rust’s fault; it’s a natural problem with porting between fairly different paradigms.)

So I kind of hacked it into working with minimal changes, producing a grotesque abomination which I’m ashamed to link to. Here’s how!

First, I got rid of the class. It turns out this makes lifetime juggling much easier right off the bat. I’m pretty sure Rust considers everything in a struct to be destroyed simultaneously (though in practice it guarantees it’ll destroy fields in order), which doesn’t leave much wiggle room. Locals within a function, on the other hand, can each have their own distinct lifetimes, which solves the problem of expressing that the borrows won’t outlive the arena.

Speaking of the arena, I solved the mutability problem there by switching to… an arena! The typed-arena crate (a port of a type used within Rust itself, I think) is an allocator — you give it a value, and it gives you back a reference, and the reference is guaranteed to be valid for as long as the arena exists. The method that does this is sneaky and takes &self rather than &mut self, so Rust doesn’t know you’re mutating the arena and won’t complain. (One drawback is that the arena will never free anything you give to it, but that’s not a big problem here.)


My next problem was with mutation. The main loop repeatedly calls possibleIntersection with pairs of segments, which can split either or both segment. Rust definitely doesn’t like that — I’d have to pass in two &muts, both of which are mutable references into the same arena, and I’d have a bunch of immutable references into that arena in the sweep list and elsewhere. This isn’t going to fly.

This is kind of a shame, and is one place where Rust seems a little overzealous. Something like this seems like it ought to be perfectly valid:

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let mut v = vec![1u32, 2u32];
let a = &mut v[0];
let b = &mut v[1];
// do stuff with a, b

The trouble is, Rust only knows the type signature, which here is something like index_mut(&'a mut self, index: usize) -> &'a T. Nothing about that says that you’re borrowing distinct elements rather than some core part of the type — and, in fact, the above code is only safe because you’re borrowing distinct elements. In the general case, Rust can’t possibly know that. It seems obvious enough from the different indexes, but nothing about the type system even says that different indexes have to return different values. And what if one were borrowed as &mut v[1] and the other were borrowed with v.iter_mut().next().unwrap()?

Anyway, this is exactly where people start to turn to RefCell — if you’re very sure you know better than Rust, then a RefCell will skirt the borrow checker while still enforcing at runtime that you don’t have more than one mutable borrow at a time.

But half the lines in this algorithm examine the endpoints of a segment! I don’t want to wrap the whole thing in a RefCell, or I’ll have to say this everywhere:

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if segment1.borrow().point.x < segment2.borrow().point.x { ... }

Gross.

But wait — this code only mutates the points themselves in one place. When a segment is split, the original segment becomes the left half, and a new segment is created to be the right half. There’s no compelling need for this; it saves an allocation for the left half, but it’s not critical to the algorithm.

Thus, I settled on a compromise. My segment type now looks like this:

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struct SegmentPacket {
    // a bunch of flags and whatnot used in the algorithm
}
struct SweepSegment {
    left_point: MapPoint,
    right_point: MapPoint,
    faces_outwards: bool,
    index: usize,
    order: usize,
    packet: RefCell<SegmentPacket>,
}

I do still need to call .borrow() or .borrow_mut() to get at the stuff in the “packet”, but that’s far less common, so there’s less noise overall. And I don’t need to wrap it in Rc because it’s part of a type that’s allocated in the arena and passed around only via references.


This still leaves me with the problem of how to actually perform the splits.

I’m not especially happy with what I came up with, I don’t know if I can defend it, and I suspect I could do much better. I changed possibleIntersection so that rather than performing splits, it returns the points at which each segment needs splitting, in the form (usize, Option<MapPoint>, Option<MapPoint>). (The usize is used as a flag for calling code and oughta be an enum, but, isn’t yet.)

Now the top-level function is responsible for all arena management, and all is well.

Except, er. possibleIntersection is called multiple times, and I don’t want to copy-paste a dozen lines of split code after each call. I tried putting just that code in its own function, which had the world’s most godawful signature, and that didn’t work because… uh… hm. I can’t remember why, exactly! Should’ve written that down.

I tried a local closure next, but closures capture their environment by reference, so now I had references to a bunch of locals for as long as the closure existed, which meant I couldn’t mutate those locals. Argh. (This seems a little silly to me, since the closure’s references cannot possibly be used for anything if the closure isn’t being called, but maybe I’m missing something. Or maybe this is just a limitation of lifetimes.)

Increasingly desperate, I tried using a macro. But… macros are hygienic, which means that any new name you use inside a macro is different from any name outside that macro. The macro thus could not see any of my locals. Usually that’s good, but here I explicitly wanted the macro to mess with my locals.

I was just about to give up and go live as a hermit in a cabin in the woods, when I discovered something quite incredible. You can define local macros! If you define a macro inside a function, then it can see any locals defined earlier in that function. Perfect!

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macro_rules! _split_segment (
    ($seg:expr, $pt:expr) => (
        {
            let pt = $pt;
            let seg = $seg;
            // ... waaay too much code ...
        }
    );
);

loop {
    // ...
    // This is possibleIntersection, renamed because Rust rightfully complains about camelCase
    let cross = handle_intersections(Some(segment), maybe_above);
    if let Some(pt) = cross.1 {
        segment = _split_segment!(segment, pt);
    }
    if let Some(pt) = cross.2 {
        maybe_above = Some(_split_segment!(maybe_above.unwrap(), pt));
    }
    // ...
}

(This doesn’t actually quite match the original algorithm, which has one case where a segment can be split twice. I realized that I could just do the left-most split, and a later iteration would perform the other split. I sure hope that’s right, anyway.)

It’s a bit ugly, and I ran into a whole lot of implicit behavior from the C++ code that I had to fix — for example, the segment is sometimes mutated just before it’s split, purely as a shortcut for mutating the left part of the split. But it finally compiles! And runs! And kinda worked, a bit!

Aftermath

I still had a lot of work to do.

For one, this code was designed for intersecting two shapes, not mass-intersecting a big pile of shapes. The basic algorithm doesn’t care about how many polygons you start with — all it sees is segments — but the code for constructing the return value needed some heavy modification.

The biggest change by far? The original code traced each segment once, expecting the result to be only a single shape. I had to change that to trace each side of each segment once, since the vast bulk of the output consists of shapes which share a side. This violated a few assumptions, which I had to hack around.

I also ran into a couple very bad edge cases, spent ages debugging them, then found out that the original algorithm had a subtle workaround that I’d commented out because it was awkward to port but didn’t seem to do anything. Whoops!

The worst was a precision error, where a vertical line could be split on a point not quite actually on the line, which wreaked all kinds of havoc. I worked around that with some tasteful rounding, which is highly dubious but makes the output more appealing to my squishy human brain. (I might switch to the original workaround, but I really dislike that even simple cases can spit out points at 1500.0000000000003. The whole thing is parameterized over the coordinate type, so maybe I could throw a rational type in there and cross my fingers?)

All that done, I finally, finally, after a couple months of intermittent progress, got what I wanted!

This is Doom 2’s MAP01. The black area to the left of center is where the player starts. Gray areas indicate where the player can walk from there, with lighter shades indicating more distant areas, where “distance” is measured by the minimum number of line crossings. Red areas can’t be reached at all.

(Note: large playable chunks of the map, including the exit room, are red. That’s because those areas are behind doors, and this code doesn’t understand doors yet.)

(Also note: The big crescent in the lower-right is also black because I was lazy and looked for the player’s starting sector by checking the bbox, and that sector’s bbox happens to match.)

The code that generated this had to go out of its way to delete all the unreachable zones around solid walls. I think I could modify the algorithm to do that on the fly pretty easily, which would probably speed it up a bit too. Downside is that the algorithm would then be pretty specifically tied to this problem, and not usable for any other kind of polygon intersection, which I would think could come up elsewhere? The modifications would be pretty minor, though, so maybe I could confine them to a closure or something.

Some final observations

It runs surprisingly slowly. Like, multiple seconds. Unless I add --release, which speeds it up by a factor of… some number with multiple digits. Wahoo. Debug mode has a high price, especially with a lot of calls in play.

The current state of this code is on GitHub. Please don’t look at it. I’m very sorry.

Honestly, most of my anguish came not from Rust, but from the original code relying on lots of fairly subtle behavior without bothering to explain what it was doing or even hint that anything unusual was going on. God, I hate C++.

I don’t know if the Rust community can learn from this. I don’t know if I even learned from this. Let’s all just quietly forget about it.

Now I just need to figure this one out…

XSStrike – Advanced XSS Fuzzer & Exploitation Suite

Post Syndicated from Darknet original https://www.darknet.org.uk/2018/03/xsstrike-advanced-xss-fuzzer-exploitation-suite/?utm_source=rss&utm_medium=social&utm_campaign=darknetfeed

XSStrike – Advanced XSS Fuzzer & Exploitation Suite

XSStrike is an advanced XSS detection suite, which contains a powerful XSS fuzzer and provides zero false positive results using fuzzy matching. XSStrike is the first XSS scanner to generate its own payloads.

It is also built in an intelligent enough manner to detect and break out of various contexts.

Features of XSStrike XSS Fuzzer & Hacking Tool

XSStrike has:

  • Powerful fuzzing engine
  • Context breaking technology
  • Intelligent payload generation
  • GET & POST method support
  • Cookie Support
  • WAF Fingerprinting
  • Handcrafted payloads for filter and WAF evasion
  • Hidden parameter discovery
  • Accurate results via levenshtein distance algorithm

There are various other XSS security related tools you can check out like:

– XSSYA v2.0 Released – XSS Vulnerability Confirmation Tool
– xssless – An Automated XSS Payload Generator Written In Python
– XSSer v1.0 – Cross Site Scripter Framework

You can download XSStrike here:

XSStrike-master.zip

Or read more here.

Read the rest of XSStrike – Advanced XSS Fuzzer & Exploitation Suite now! Only available at Darknet.

AWS Online Tech Talks – January 2018

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-january-2018/

Happy New Year! Kick of 2018 right by expanding your AWS knowledge with a great batch of new Tech Talks. We’re covering some of the biggest launches from re:Invent including Amazon Neptune, Amazon Rekognition Video, AWS Fargate, AWS Cloud9, Amazon Kinesis Video Streams, AWS PrivateLink, AWS Single-Sign On and more!

January 2018– Schedule

Noted below are the upcoming scheduled live, online technical sessions being held during the month of January. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts.

Webinars featured this month are:

Monday January 22

Analytics & Big Data
11:00 AM – 11:45 AM PT Analyze your Data Lake, Fast @ Any Scale  Lvl 300

Database
01:00 PM – 01:45 PM PT Deep Dive on Amazon Neptune Lvl 200

Tuesday, January 23

Artificial Intelligence
9:00 AM – 09:45 AM PT  How to get the most out of Amazon Rekognition Video, a deep learning based video analysis service Lvl 300

Containers

11:00 AM – 11:45 AM Introducing AWS Fargate Lvl 200

Serverless
01:00 PM – 02:00 PM PT Overview of Serverless Application Deployment Patterns Lvl 400

Wednesday, January 24

DevOps
09:00 AM – 09:45 AM PT Introducing AWS Cloud9  Lvl 200

Analytics & Big Data
11:00 AM – 11:45 AM PT Deep Dive: Amazon Kinesis Video Streams
Lvl 300
Database
01:00 PM – 01:45 PM PT Introducing Amazon Aurora with PostgreSQL Compatibility Lvl 200

Thursday, January 25

Artificial Intelligence
09:00 AM – 09:45 AM PT Introducing Amazon SageMaker Lvl 200

Mobile
11:00 AM – 11:45 AM PT Ionic and React Hybrid Web/Native Mobile Applications with Mobile Hub Lvl 200

IoT
01:00 PM – 01:45 PM PT Connected Product Development: Secure Cloud & Local Connectivity for Microcontroller-based Devices Lvl 200

Monday, January 29

Enterprise
11:00 AM – 11:45 AM PT Enterprise Solutions Best Practices 100 Achieving Business Value with AWS Lvl 100

Compute
01:00 PM – 01:45 PM PT Introduction to Amazon Lightsail Lvl 200

Tuesday, January 30

Security, Identity & Compliance
09:00 AM – 09:45 AM PT Introducing Managed Rules for AWS WAF Lvl 200

Storage
11:00 AM – 11:45 AM PT  Improving Backup & DR – AWS Storage Gateway Lvl 300

Compute
01:00 PM – 01:45 PM PT  Introducing the New Simplified Access Model for EC2 Spot Instances Lvl 200

Wednesday, January 31

Networking
09:00 AM – 09:45 AM PT  Deep Dive on AWS PrivateLink Lvl 300

Enterprise
11:00 AM – 11:45 AM PT Preparing Your Team for a Cloud Transformation Lvl 200

Compute
01:00 PM – 01:45 PM PT  The Nitro Project: Next-Generation EC2 Infrastructure Lvl 300

Thursday, February 1

Security, Identity & Compliance
09:00 AM – 09:45 AM PT  Deep Dive on AWS Single Sign-On Lvl 300

Storage
11:00 AM – 11:45 AM PT How to Build a Data Lake in Amazon S3 & Amazon Glacier Lvl 300

The Top 10 Most Downloaded AWS Security and Compliance Documents in 2017

Post Syndicated from Sara Duffer original https://aws.amazon.com/blogs/security/the-top-10-most-downloaded-aws-security-and-compliance-documents-in-2017/

AWS download logo

The following list includes the ten most downloaded AWS security and compliance documents in 2017. Using this list, you can learn about what other AWS customers found most interesting about security and compliance last year.

  1. AWS Security Best Practices – This guide is intended for customers who are designing the security infrastructure and configuration for applications running on AWS. The guide provides security best practices that will help you define your Information Security Management System (ISMS) and build a set of security policies and processes for your organization so that you can protect your data and assets in the AWS Cloud.
  2. AWS: Overview of Security Processes – This whitepaper describes the physical and operational security processes for the AWS managed network and infrastructure, and helps answer questions such as, “How does AWS help me protect my data?”
  3. Architecting for HIPAA Security and Compliance on AWS – This whitepaper describes how to leverage AWS to develop applications that meet HIPAA and HITECH compliance requirements.
  4. Service Organization Controls (SOC) 3 Report – This publicly available report describes internal AWS security controls, availability, processing integrity, confidentiality, and privacy.
  5. Introduction to AWS Security –This document provides an introduction to AWS’s approach to security, including the controls in the AWS environment, and some of the products and features that AWS makes available to customers to meet your security objectives.
  6. AWS Best Practices for DDoS Resiliency – This whitepaper covers techniques to mitigate distributed denial of service (DDoS) attacks.
  7. AWS: Risk and Compliance – This whitepaper provides information to help customers integrate AWS into their existing control framework, including a basic approach for evaluating AWS controls and a description of AWS certifications, programs, reports, and third-party attestations.
  8. Use AWS WAF to Mitigate OWASP’s Top 10 Web Application Vulnerabilities – AWS WAF is a web application firewall that helps you protect your websites and web applications against various attack vectors at the HTTP protocol level. This whitepaper outlines how you can use AWS WAF to mitigate the application vulnerabilities that are defined in the Open Web Application Security Project (OWASP) Top 10 list of most common categories of application security flaws.
  9. Introduction to Auditing the Use of AWS – This whitepaper provides information, tools, and approaches for auditors to use when auditing the security of the AWS managed network and infrastructure.
  10. AWS Security and Compliance: Quick Reference Guide – By using AWS, you inherit the many security controls that we operate, thus reducing the number of security controls that you need to maintain. Your own compliance and certification programs are strengthened while at the same time lowering your cost to maintain and run your specific security assurance requirements. Learn more in this quick reference guide.

– Sara

Now Open AWS EU (Paris) Region

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-open-aws-eu-paris-region/

Today we are launching our 18th AWS Region, our fourth in Europe. Located in the Paris area, AWS customers can use this Region to better serve customers in and around France.

The Details
The new EU (Paris) Region provides a broad suite of AWS services including Amazon API Gateway, Amazon Aurora, Amazon CloudFront, Amazon CloudWatch, CloudWatch Events, Amazon CloudWatch Logs, Amazon DynamoDB, Amazon Elastic Compute Cloud (EC2), EC2 Container Registry, Amazon ECS, Amazon Elastic Block Store (EBS), Amazon EMR, Amazon ElastiCache, Amazon Elasticsearch Service, Amazon Glacier, Amazon Kinesis Streams, Polly, Amazon Redshift, Amazon Relational Database Service (RDS), Amazon Route 53, Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), Amazon Simple Storage Service (S3), Amazon Simple Workflow Service (SWF), Amazon Virtual Private Cloud, Auto Scaling, AWS Certificate Manager (ACM), AWS CloudFormation, AWS CloudTrail, AWS CodeDeploy, AWS Config, AWS Database Migration Service, AWS Direct Connect, AWS Elastic Beanstalk, AWS Identity and Access Management (IAM), AWS Key Management Service (KMS), AWS Lambda, AWS Marketplace, AWS OpsWorks Stacks, AWS Personal Health Dashboard, AWS Server Migration Service, AWS Service Catalog, AWS Shield Standard, AWS Snowball, AWS Snowball Edge, AWS Snowmobile, AWS Storage Gateway, AWS Support (including AWS Trusted Advisor), Elastic Load Balancing, and VM Import.

The Paris Region supports all sizes of C5, M5, R4, T2, D2, I3, and X1 instances.

There are also four edge locations for Amazon Route 53 and Amazon CloudFront: three in Paris and one in Marseille, all with AWS WAF and AWS Shield. Check out the AWS Global Infrastructure page to learn more about current and future AWS Regions.

The Paris Region will benefit from three AWS Direct Connect locations. Telehouse Voltaire is available today. AWS Direct Connect will also become available at Equinix Paris in early 2018, followed by Interxion Paris.

All AWS infrastructure regions around the world are designed, built, and regularly audited to meet the most rigorous compliance standards and to provide high levels of security for all AWS customers. These include ISO 27001, ISO 27017, ISO 27018, SOC 1 (Formerly SAS 70), SOC 2 and SOC 3 Security & Availability, PCI DSS Level 1, and many more. This means customers benefit from all the best practices of AWS policies, architecture, and operational processes built to satisfy the needs of even the most security sensitive customers.

AWS is certified under the EU-US Privacy Shield, and the AWS Data Processing Addendum (DPA) is GDPR-ready and available now to all AWS customers to help them prepare for May 25, 2018 when the GDPR becomes enforceable. The current AWS DPA, as well as the AWS GDPR DPA, allows customers to transfer personal data to countries outside the European Economic Area (EEA) in compliance with European Union (EU) data protection laws. AWS also adheres to the Cloud Infrastructure Service Providers in Europe (CISPE) Code of Conduct. The CISPE Code of Conduct helps customers ensure that AWS is using appropriate data protection standards to protect their data, consistent with the GDPR. In addition, AWS offers a wide range of services and features to help customers meet the requirements of the GDPR, including services for access controls, monitoring, logging, and encryption.

From Our Customers
Many AWS customers are preparing to use this new Region. Here’s a small sample:

Societe Generale, one of the largest banks in France and the world, has accelerated their digital transformation while working with AWS. They developed SG Research, an application that makes reports from Societe Generale’s analysts available to corporate customers in order to improve the decision-making process for investments. The new AWS Region will reduce latency between applications running in the cloud and in their French data centers.

SNCF is the national railway company of France. Their mobile app, powered by AWS, delivers real-time traffic information to 14 million riders. Extreme weather, traffic events, holidays, and engineering works can cause usage to peak at hundreds of thousands of users per second. They are planning to use machine learning and big data to add predictive features to the app.

Radio France, the French public radio broadcaster, offers seven national networks, and uses AWS to accelerate its innovation and stay competitive.

Les Restos du Coeur, a French charity that provides assistance to the needy, delivering food packages and participating in their social and economic integration back into French society. Les Restos du Coeur is using AWS for its CRM system to track the assistance given to each of their beneficiaries and the impact this is having on their lives.

AlloResto by JustEat (a leader in the French FoodTech industry), is using AWS to to scale during traffic peaks and to accelerate their innovation process.

AWS Consulting and Technology Partners
We are already working with a wide variety of consulting, technology, managed service, and Direct Connect partners in France. Here’s a partial list:

AWS Premier Consulting PartnersAccenture, Capgemini, Claranet, CloudReach, DXC, and Edifixio.

AWS Consulting PartnersABC Systemes, Atos International SAS, CoreExpert, Cycloid, Devoteam, LINKBYNET, Oxalide, Ozones, Scaleo Information Systems, and Sopra Steria.

AWS Technology PartnersAxway, Commerce Guys, MicroStrategy, Sage, Software AG, Splunk, Tibco, and Zerolight.

AWS in France
We have been investing in Europe, with a focus on France, for the last 11 years. We have also been developing documentation and training programs to help our customers to improve their skills and to accelerate their journey to the AWS Cloud.

As part of our commitment to AWS customers in France, we plan to train more than 25,000 people in the coming years, helping them develop highly sought after cloud skills. They will have access to AWS training resources in France via AWS Academy, AWSome days, AWS Educate, and webinars, all delivered in French by AWS Technical Trainers and AWS Certified Trainers.

Use it Today
The EU (Paris) Region is open for business now and you can start using it today!

Jeff;