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Monitoring your Amazon SNS message filtering activity with Amazon CloudWatch

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/monitoring-your-amazon-sns-message-filtering-activity-with-amazon-cloudwatch/

This post is courtesy of Otavio Ferreira, Manager, Amazon SNS, AWS Messaging.

Amazon SNS message filtering provides a set of string and numeric matching operators that allow each subscription to receive only the messages of interest. Hence, SNS message filtering can simplify your pub/sub messaging architecture by offloading the message filtering logic from your subscriber systems, as well as the message routing logic from your publisher systems.

After you set the subscription attribute that defines a filter policy, the subscribing endpoint receives only the messages that carry attributes matching this filter policy. Other messages published to the topic are filtered out for this subscription. In this way, the native integration between SNS and Amazon CloudWatch provides visibility into the number of messages delivered, as well as the number of messages filtered out.

CloudWatch metrics are captured automatically for you. To get started with SNS message filtering, see Filtering Messages with Amazon SNS.

Message Filtering Metrics

The following six CloudWatch metrics are relevant to understanding your SNS message filtering activity:

  • NumberOfMessagesPublished – Inbound traffic to SNS. This metric tracks all the messages that have been published to the topic.
  • NumberOfNotificationsDelivered – Outbound traffic from SNS. This metric tracks all the messages that have been successfully delivered to endpoints subscribed to the topic. A delivery takes place either when the incoming message attributes match a subscription filter policy, or when the subscription has no filter policy at all, which results in a catch-all behavior.
  • NumberOfNotificationsFilteredOut – This metric tracks all the messages that were filtered out because they carried attributes that didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-NoMessageAttributes – This metric tracks all the messages that were filtered out because they didn’t carry any attributes at all and, consequently, didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-InvalidAttributes – This metric keeps track of messages that were filtered out because they carried invalid or malformed attributes and, thus, didn’t match the subscription filter policy.
  • NumberOfNotificationsFailed – This last metric tracks all the messages that failed to be delivered to subscribing endpoints, regardless of whether a filter policy had been set for the endpoint. This metric is emitted after the message delivery retry policy is exhausted, and SNS stops attempting to deliver the message. At that moment, the subscribing endpoint is likely no longer reachable. For example, the subscribing SQS queue or Lambda function has been deleted by its owner. You may want to closely monitor this metric to address message delivery issues quickly.

Message filtering graphs

Through the AWS Management Console, you can compose graphs to display your SNS message filtering activity. The graph shows the number of messages published, delivered, and filtered out within the timeframe you specify (1h, 3h, 12h, 1d, 3d, 1w, or custom).

SNS message filtering for CloudWatch Metrics

To compose an SNS message filtering graph with CloudWatch:

  1. Open the CloudWatch console.
  2. Choose Metrics, SNS, All Metrics, and Topic Metrics.
  3. Select all metrics to add to the graph, such as:
    • NumberOfMessagesPublished
    • NumberOfNotificationsDelivered
    • NumberOfNotificationsFilteredOut
  4. Choose Graphed metrics.
  5. In the Statistic column, switch from Average to Sum.
  6. Title your graph with a descriptive name, such as “SNS Message Filtering”

After you have your graph set up, you may want to copy the graph link for bookmarking, emailing, or sharing with co-workers. You may also want to add your graph to a CloudWatch dashboard for easy access in the future. Both actions are available to you on the Actions menu, which is found above the graph.

Summary

SNS message filtering defines how SNS topics behave in terms of message delivery. By using CloudWatch metrics, you gain visibility into the number of messages published, delivered, and filtered out. This enables you to validate the operation of filter policies and more easily troubleshoot during development phases.

SNS message filtering can be implemented easily with existing AWS SDKs by applying message and subscription attributes across all SNS supported protocols (Amazon SQS, AWS Lambda, HTTP, SMS, email, and mobile push). CloudWatch metrics for SNS message filtering is available now, in all AWS Regions.

For information about pricing, see the CloudWatch pricing page.

For more information, see:

Measuring the throughput for Amazon MQ using the JMS Benchmark

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/measuring-the-throughput-for-amazon-mq-using-the-jms-benchmark/

This post is courtesy of Alan Protasio, Software Development Engineer, Amazon Web Services

Just like compute and storage, messaging is a fundamental building block of enterprise applications. Message brokers (aka “message-oriented middleware”) enable different software systems, often written in different languages, on different platforms, running in different locations, to communicate and exchange information. Mission-critical applications, such as CRM and ERP, rely on message brokers to work.

A common performance consideration for customers deploying a message broker in a production environment is the throughput of the system, measured as messages per second. This is important to know so that application environments (hosts, threads, memory, etc.) can be configured correctly.

In this post, we demonstrate how to measure the throughput for Amazon MQ, a new managed message broker service for ActiveMQ, using JMS Benchmark. It should take between 15–20 minutes to set up the environment and an hour to run the benchmark. We also provide some tips on how to configure Amazon MQ for optimal throughput.

Benchmarking throughput for Amazon MQ

ActiveMQ can be used for a number of use cases. These use cases can range from simple fire and forget tasks (that is, asynchronous processing), low-latency request-reply patterns, to buffering requests before they are persisted to a database.

The throughput of Amazon MQ is largely dependent on the use case. For example, if you have non-critical workloads such as gathering click events for a non-business-critical portal, you can use ActiveMQ in a non-persistent mode and get extremely high throughput with Amazon MQ.

On the flip side, if you have a critical workload where durability is extremely important (meaning that you can’t lose a message), then you are bound by the I/O capacity of your underlying persistence store. We recommend using mq.m4.large for the best results. The mq.t2.micro instance type is intended for product evaluation. Performance is limited, due to the lower memory and burstable CPU performance.

Tip: To improve your throughput with Amazon MQ, make sure that you have consumers processing messaging as fast as (or faster than) your producers are pushing messages.

Because it’s impossible to talk about how the broker (ActiveMQ) behaves for each and every use case, we walk through how to set up your own benchmark for Amazon MQ using our favorite open-source benchmarking tool: JMS Benchmark. We are fans of the JMS Benchmark suite because it’s easy to set up and deploy, and comes with a built-in visualizer of the results.

Non-Persistent Scenarios – Queue latency as you scale producer throughput

JMS Benchmark nonpersistent scenarios

Getting started

At the time of publication, you can create an mq.m4.large single-instance broker for testing for $0.30 per hour (US pricing).

This walkthrough covers the following tasks:

  1.  Create and configure the broker.
  2. Create an EC2 instance to run your benchmark
  3. Configure the security groups
  4.  Run the benchmark.

Step 1 – Create and configure the broker
Create and configure the broker using Tutorial: Creating and Configuring an Amazon MQ Broker.

Step 2 – Create an EC2 instance to run your benchmark
Launch the EC2 instance using Step 1: Launch an Instance. We recommend choosing the m5.large instance type.

Step 3 – Configure the security groups
Make sure that all the security groups are correctly configured to let the traffic flow between the EC2 instance and your broker.

  1. Sign in to the Amazon MQ console.
  2. From the broker list, choose the name of your broker (for example, MyBroker)
  3. In the Details section, under Security and network, choose the name of your security group or choose the expand icon ( ).
  4. From the security group list, choose your security group.
  5. At the bottom of the page, choose Inbound, Edit.
  6. In the Edit inbound rules dialog box, add a role to allow traffic between your instance and the broker:
    • Choose Add Rule.
    • For Type, choose Custom TCP.
    • For Port Range, type the ActiveMQ SSL port (61617).
    • For Source, leave Custom selected and then type the security group of your EC2 instance.
    • Choose Save.

Your broker can now accept the connection from your EC2 instance.

Step 4 – Run the benchmark
Connect to your EC2 instance using SSH and run the following commands:

$ cd ~
$ curl -L https://github.com/alanprot/jms-benchmark/archive/master.zip -o master.zip
$ unzip master.zip
$ cd jms-benchmark-master
$ chmod a+x bin/*
$ env \
  SERVER_SETUP=false \
  SERVER_ADDRESS={activemq-endpoint} \
  ACTIVEMQ_TRANSPORT=ssl\
  ACTIVEMQ_PORT=61617 \
  ACTIVEMQ_USERNAME={activemq-user} \
  ACTIVEMQ_PASSWORD={activemq-password} \
  ./bin/benchmark-activemq

After the benchmark finishes, you can find the results in the ~/reports directory. As you may notice, the performance of ActiveMQ varies based on the number of consumers, producers, destinations, and message size.

Amazon MQ architecture

The last bit that’s important to know so that you can better understand the results of the benchmark is how Amazon MQ is architected.

Amazon MQ is architected to be highly available (HA) and durable. For HA, we recommend using the multi-AZ option. After a message is sent to Amazon MQ in persistent mode, the message is written to the highly durable message store that replicates the data across multiple nodes in multiple Availability Zones. Because of this replication, for some use cases you may see a reduction in throughput as you migrate to Amazon MQ. Customers have told us they appreciate the benefits of message replication as it helps protect durability even in the face of the loss of an Availability Zone.

Conclusion

We hope this gives you an idea of how Amazon MQ performs. We encourage you to run tests to simulate your own use cases.

To learn more, see the Amazon MQ website. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

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.

Ransomware Update: Viruses Targeting Business IT Servers

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/ransomware-update-viruses-targeting-business-it-servers/

Ransomware warning message on computer

As ransomware attacks have grown in number in recent months, the tactics and attack vectors also have evolved. While the primary method of attack used to be to target individual computer users within organizations with phishing emails and infected attachments, we’re increasingly seeing attacks that target weaknesses in businesses’ IT infrastructure.

How Ransomware Attacks Typically Work

In our previous posts on ransomware, we described the common vehicles used by hackers to infect organizations with ransomware viruses. Most often, downloaders distribute trojan horses through malicious downloads and spam emails. The emails contain a variety of file attachments, which if opened, will download and run one of the many ransomware variants. Once a user’s computer is infected with a malicious downloader, it will retrieve additional malware, which frequently includes crypto-ransomware. After the files have been encrypted, a ransom payment is demanded of the victim in order to decrypt the files.

What’s Changed With the Latest Ransomware Attacks?

In 2016, a customized ransomware strain called SamSam began attacking the servers in primarily health care institutions. SamSam, unlike more conventional ransomware, is not delivered through downloads or phishing emails. Instead, the attackers behind SamSam use tools to identify unpatched servers running Red Hat’s JBoss enterprise products. Once the attackers have successfully gained entry into one of these servers by exploiting vulnerabilities in JBoss, they use other freely available tools and scripts to collect credentials and gather information on networked computers. Then they deploy their ransomware to encrypt files on these systems before demanding a ransom. Gaining entry to an organization through its IT center rather than its endpoints makes this approach scalable and especially unsettling.

SamSam’s methodology is to scour the Internet searching for accessible and vulnerable JBoss application servers, especially ones used by hospitals. It’s not unlike a burglar rattling doorknobs in a neighborhood to find unlocked homes. When SamSam finds an unlocked home (unpatched server), the software infiltrates the system. It is then free to spread across the company’s network by stealing passwords. As it transverses the network and systems, it encrypts files, preventing access until the victims pay the hackers a ransom, typically between $10,000 and $15,000. The low ransom amount has encouraged some victimized organizations to pay the ransom rather than incur the downtime required to wipe and reinitialize their IT systems.

The success of SamSam is due to its effectiveness rather than its sophistication. SamSam can enter and transverse a network without human intervention. Some organizations are learning too late that securing internet-facing services in their data center from attack is just as important as securing endpoints.

The typical steps in a SamSam ransomware attack are:

1
Attackers gain access to vulnerable server
Attackers exploit vulnerable software or weak/stolen credentials.
2
Attack spreads via remote access tools
Attackers harvest credentials, create SOCKS proxies to tunnel traffic, and abuse RDP to install SamSam on more computers in the network.
3
Ransomware payload deployed
Attackers run batch scripts to execute ransomware on compromised machines.
4
Ransomware demand delivered requiring payment to decrypt files
Demand amounts vary from victim to victim. Relatively low ransom amounts appear to be designed to encourage quick payment decisions.

What all the organizations successfully exploited by SamSam have in common is that they were running unpatched servers that made them vulnerable to SamSam. Some organizations had their endpoints and servers backed up, while others did not. Some of those without backups they could use to recover their systems chose to pay the ransom money.

Timeline of SamSam History and Exploits

Since its appearance in 2016, SamSam has been in the news with many successful incursions into healthcare, business, and government institutions.

March 2016
SamSam appears

SamSam campaign targets vulnerable JBoss servers
Attackers hone in on healthcare organizations specifically, as they’re more likely to have unpatched JBoss machines.

April 2016
SamSam finds new targets

SamSam begins targeting schools and government.
After initial success targeting healthcare, attackers branch out to other sectors.

April 2017
New tactics include RDP

Attackers shift to targeting organizations with exposed RDP connections, and maintain focus on healthcare.
An attack on Erie County Medical Center costs the hospital $10 million over three months of recovery.
Erie County Medical Center attacked by SamSam ransomware virus

January 2018
Municipalities attacked

• Attack on Municipality of Farmington, NM.
• Attack on Hancock Health.
Hancock Regional Hospital notice following SamSam attack
• Attack on Adams Memorial Hospital
• Attack on Allscripts (Electronic Health Records), which includes 180,000 physicians, 2,500 hospitals, and 7.2 million patients’ health records.

February 2018
Attack volume increases

• Attack on Davidson County, NC.
• Attack on Colorado Department of Transportation.
SamSam virus notification

March 2018
SamSam shuts down Atlanta

• Second attack on Colorado Department of Transportation.
• City of Atlanta suffers a devastating attack by SamSam.
The attack has far-reaching impacts — crippling the court system, keeping residents from paying their water bills, limiting vital communications like sewer infrastructure requests, and pushing the Atlanta Police Department to file paper reports.
Atlanta Ransomware outage alert
• SamSam campaign nets $325,000 in 4 weeks.
Infections spike as attackers launch new campaigns. Healthcare and government organizations are once again the primary targets.

How to Defend Against SamSam and Other Ransomware Attacks

The best way to respond to a ransomware attack is to avoid having one in the first place. If you are attacked, making sure your valuable data is backed up and unreachable by ransomware infection will ensure that your downtime and data loss will be minimal or none if you ever suffer an attack.

In our previous post, How to Recover From Ransomware, we listed the ten ways to protect your organization from ransomware.

  1. Use anti-virus and anti-malware software or other security policies to block known payloads from launching.
  2. Make frequent, comprehensive backups of all important files and isolate them from local and open networks. Cybersecurity professionals view data backup and recovery (74% in a recent survey) by far as the most effective solution to respond to a successful ransomware attack.
  3. Keep offline backups of data stored in locations inaccessible from any potentially infected computer, such as disconnected external storage drives or the cloud, which prevents them from being accessed by the ransomware.
  4. Install the latest security updates issued by software vendors of your OS and applications. Remember to patch early and patch often to close known vulnerabilities in operating systems, server software, browsers, and web plugins.
  5. Consider deploying security software to protect endpoints, email servers, and network systems from infection.
  6. Exercise cyber hygiene, such as using caution when opening email attachments and links.
  7. Segment your networks to keep critical computers isolated and to prevent the spread of malware in case of attack. Turn off unneeded network shares.
  8. Turn off admin rights for users who don’t require them. Give users the lowest system permissions they need to do their work.
  9. Restrict write permissions on file servers as much as possible.
  10. Educate yourself, your employees, and your family in best practices to keep malware out of your systems. Update everyone on the latest email phishing scams and human engineering aimed at turning victims into abettors.

Please Tell Us About Your Experiences with Ransomware

Have you endured a ransomware attack or have a strategy to avoid becoming a victim? Please tell us of your experiences in the comments.

The post Ransomware Update: Viruses Targeting Business IT Servers appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Securing Elections

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/securing_electi_1.html

Elections serve two purposes. The first, and obvious, purpose is to accurately choose the winner. But the second is equally important: to convince the loser. To the extent that an election system is not transparently and auditably accurate, it fails in that second purpose. Our election systems are failing, and we need to fix them.

Today, we conduct our elections on computers. Our registration lists are in computer databases. We vote on computerized voting machines. And our tabulation and reporting is done on computers. We do this for a lot of good reasons, but a side effect is that elections now have all the insecurities inherent in computers. The only way to reliably protect elections from both malice and accident is to use something that is not hackable or unreliable at scale; the best way to do that is to back up as much of the system as possible with paper.

Recently, there have been two graphic demonstrations of how bad our computerized voting system is. In 2007, the states of California and Ohio conducted audits of their electronic voting machines. Expert review teams found exploitable vulnerabilities in almost every component they examined. The researchers were able to undetectably alter vote tallies, erase audit logs, and load malware on to the systems. Some of their attacks could be implemented by a single individual with no greater access than a normal poll worker; others could be done remotely.

Last year, the Defcon hackers’ conference sponsored a Voting Village. Organizers collected 25 pieces of voting equipment, including voting machines and electronic poll books. By the end of the weekend, conference attendees had found ways to compromise every piece of test equipment: to load malicious software, compromise vote tallies and audit logs, or cause equipment to fail.

It’s important to understand that these were not well-funded nation-state attackers. These were not even academics who had been studying the problem for weeks. These were bored hackers, with no experience with voting machines, playing around between parties one weekend.

It shouldn’t be any surprise that voting equipment, including voting machines, voter registration databases, and vote tabulation systems, are that hackable. They’re computers — often ancient computers running operating systems no longer supported by the manufacturers — and they don’t have any magical security technology that the rest of the industry isn’t privy to. If anything, they’re less secure than the computers we generally use, because their manufacturers hide any flaws behind the proprietary nature of their equipment.

We’re not just worried about altering the vote. Sometimes causing widespread failures, or even just sowing mistrust in the system, is enough. And an election whose results are not trusted or believed is a failed election.

Voting systems have another requirement that makes security even harder to achieve: the requirement for a secret ballot. Because we have to securely separate the election-roll system that determines who can vote from the system that collects and tabulates the votes, we can’t use the security systems available to banking and other high-value applications.

We can securely bank online, but can’t securely vote online. If we could do away with anonymity — if everyone could check that their vote was counted correctly — then it would be easy to secure the vote. But that would lead to other problems. Before the US had the secret ballot, voter coercion and vote-buying were widespread.

We can’t, so we need to accept that our voting systems are insecure. We need an election system that is resilient to the threats. And for many parts of the system, that means paper.

Let’s start with the voter rolls. We know they’ve already been targeted. In 2016, someone changed the party affiliation of hundreds of voters before the Republican primary. That’s just one possibility. A well-executed attack that deletes, for example, one in five voters at random — or changes their addresses — would cause chaos on election day.

Yes, we need to shore up the security of these systems. We need better computer, network, and database security for the various state voter organizations. We also need to better secure the voter registration websites, with better design and better internet security. We need better security for the companies that build and sell all this equipment.

Multiple, unchangeable backups are essential. A record of every addition, deletion, and change needs to be stored on a separate system, on write-only media like a DVD. Copies of that DVD, or — even better — a paper printout of the voter rolls, should be available at every polling place on election day. We need to be ready for anything.

Next, the voting machines themselves. Security researchers agree that the gold standard is a voter-verified paper ballot. The easiest (and cheapest) way to achieve this is through optical-scan voting. Voters mark paper ballots by hand; they are fed into a machine and counted automatically. That paper ballot is saved, and serves as a final true record in a recount in case of problems. Touch-screen machines that print a paper ballot to drop in a ballot box can also work for voters with disabilities, as long as the ballot can be easily read and verified by the voter.

Finally, the tabulation and reporting systems. Here again we need more security in the process, but we must always use those paper ballots as checks on the computers. A manual, post-election, risk-limiting audit varies the number of ballots examined according to the margin of victory. Conducting this audit after every election, before the results are certified, gives us confidence that the election outcome is correct, even if the voting machines and tabulation computers have been tampered with. Additionally, we need better coordination and communications when incidents occur.

It’s vital to agree on these procedures and policies before an election. Before the fact, when anyone can win and no one knows whose votes might be changed, it’s easy to agree on strong security. But after the vote, someone is the presumptive winner — and then everything changes. Half of the country wants the result to stand, and half wants it reversed. At that point, it’s too late to agree on anything.

The politicians running in the election shouldn’t have to argue their challenges in court. Getting elections right is in the interest of all citizens. Many countries have independent election commissions that are charged with conducting elections and ensuring their security. We don’t do that in the US.

Instead, we have representatives from each of our two parties in the room, keeping an eye on each other. That provided acceptable security against 20th-century threats, but is totally inadequate to secure our elections in the 21st century. And the belief that the diversity of voting systems in the US provides a measure of security is a dangerous myth, because few districts can be decisive and there are so few voting-machine vendors.

We can do better. In 2017, the Department of Homeland Security declared elections to be critical infrastructure, allowing the department to focus on securing them. On 23 March, Congress allocated $380m to states to upgrade election security.

These are good starts, but don’t go nearly far enough. The constitution delegates elections to the states but allows Congress to “make or alter such Regulations”. In 1845, Congress set a nationwide election day. Today, we need it to set uniform and strict election standards.

This essay originally appeared in the Guardian.

Implementing safe AWS Lambda deployments with AWS CodeDeploy

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/implementing-safe-aws-lambda-deployments-with-aws-codedeploy/

This post courtesy of George Mao, AWS Senior Serverless Specialist – Solutions Architect

AWS Lambda and AWS CodeDeploy recently made it possible to automatically shift incoming traffic between two function versions based on a preconfigured rollout strategy. This new feature allows you to gradually shift traffic to the new function. If there are any issues with the new code, you can quickly rollback and control the impact to your application.

Previously, you had to manually move 100% of traffic from the old version to the new version. Now, you can have CodeDeploy automatically execute pre- or post-deployment tests and automate a gradual rollout strategy. Traffic shifting is built right into the AWS Serverless Application Model (SAM), making it easy to define and deploy your traffic shifting capabilities. SAM is an extension of AWS CloudFormation that provides a simplified way of defining serverless applications.

In this post, I show you how to use SAM, CloudFormation, and CodeDeploy to accomplish an automated rollout strategy for safe Lambda deployments.

Scenario

For this walkthrough, you write a Lambda application that returns a count of the S3 buckets that you own. You deploy it and use it in production. Later on, you receive requirements that tell you that you need to change your Lambda application to count only buckets that begin with the letter “a”.

Before you make the change, you need to be sure that your new Lambda application works as expected. If it does have issues, you want to minimize the number of impacted users and roll back easily. To accomplish this, you create a deployment process that publishes the new Lambda function, but does not send any traffic to it. You use CodeDeploy to execute a PreTraffic test to ensure that your new function works as expected. After the test succeeds, CodeDeploy automatically shifts traffic gradually to the new version of the Lambda function.

Your Lambda function is exposed as a REST service via an Amazon API Gateway deployment. This makes it easy to test and integrate.

Prerequisites

To execute the SAM and CloudFormation deployment, you must have the following IAM permissions:

  • cloudformation:*
  • lambda:*
  • codedeploy:*
  • iam:create*

You may use the AWS SAM Local CLI or the AWS CLI to package and deploy your Lambda application. If you choose to use SAM Local, be sure to install it onto your system. For more information, see AWS SAM Local Installation.

All of the code used in this post can be found in this GitHub repository: https://github.com/aws-samples/aws-safe-lambda-deployments.

Walkthrough

For this post, use SAM to define your resources because it comes with built-in CodeDeploy support for safe Lambda deployments.  The deployment is handled and automated by CloudFormation.

SAM allows you to define your Serverless applications in a simple and concise fashion, because it automatically creates all necessary resources behind the scenes. For example, if you do not define an execution role for a Lambda function, SAM automatically creates one. SAM also creates the CodeDeploy application necessary to drive the traffic shifting, as well as the IAM service role that CodeDeploy uses to execute all actions.

Create a SAM template

To get started, write your SAM template and call it template.yaml.

AWSTemplateFormatVersion : '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Description: An example SAM template for Lambda Safe Deployments.

Resources:

  returnS3Buckets:
    Type: AWS::Serverless::Function
    Properties:
      Handler: returnS3Buckets.handler
      Runtime: nodejs6.10
      AutoPublishAlias: live
      Policies:
        - Version: "2012-10-17"
          Statement: 
          - Effect: "Allow"
            Action: 
              - "s3:ListAllMyBuckets"
            Resource: '*'
      DeploymentPreference:
          Type: Linear10PercentEvery1Minute
          Hooks:
            PreTraffic: !Ref preTrafficHook
      Events:
        Api:
          Type: Api
          Properties:
            Path: /test
            Method: get

  preTrafficHook:
    Type: AWS::Serverless::Function
    Properties:
      Handler: preTrafficHook.handler
      Policies:
        - Version: "2012-10-17"
          Statement: 
          - Effect: "Allow"
            Action: 
              - "codedeploy:PutLifecycleEventHookExecutionStatus"
            Resource:
              !Sub 'arn:aws:codedeploy:${AWS::Region}:${AWS::AccountId}:deploymentgroup:${ServerlessDeploymentApplication}/*'
        - Version: "2012-10-17"
          Statement: 
          - Effect: "Allow"
            Action: 
              - "lambda:InvokeFunction"
            Resource: !Ref returnS3Buckets.Version
      Runtime: nodejs6.10
      FunctionName: 'CodeDeployHook_preTrafficHook'
      DeploymentPreference:
        Enabled: false
      Timeout: 5
      Environment:
        Variables:
          NewVersion: !Ref returnS3Buckets.Version

This template creates two functions:

  • returnS3Buckets
  • preTrafficHook

The returnS3Buckets function is where your application logic lives. It’s a simple piece of code that uses the AWS SDK for JavaScript in Node.JS to call the Amazon S3 listBuckets API action and return the number of buckets.

'use strict';

var AWS = require('aws-sdk');
var s3 = new AWS.S3();

exports.handler = (event, context, callback) => {
	console.log("I am here! " + context.functionName  +  ":"  +  context.functionVersion);

	s3.listBuckets(function (err, data){
		if(err){
			console.log(err, err.stack);
			callback(null, {
				statusCode: 500,
				body: "Failed!"
			});
		}
		else{
			var allBuckets = data.Buckets;

			console.log("Total buckets: " + allBuckets.length);
			callback(null, {
				statusCode: 200,
				body: allBuckets.length
			});
		}
	});	
}

Review the key parts of the SAM template that defines returnS3Buckets:

  • The AutoPublishAlias attribute instructs SAM to automatically publish a new version of the Lambda function for each new deployment and link it to the live alias.
  • The Policies attribute specifies additional policy statements that SAM adds onto the automatically generated IAM role for this function. The first statement provides the function with permission to call listBuckets.
  • The DeploymentPreference attribute configures the type of rollout pattern to use. In this case, you are shifting traffic in a linear fashion, moving 10% of traffic every minute to the new version. For more information about supported patterns, see Serverless Application Model: Traffic Shifting Configurations.
  • The Hooks attribute specifies that you want to execute the preTrafficHook Lambda function before CodeDeploy automatically begins shifting traffic. This function should perform validation testing on the newly deployed Lambda version. This function invokes the new Lambda function and checks the results. If you’re satisfied with the tests, instruct CodeDeploy to proceed with the rollout via an API call to: codedeploy.putLifecycleEventHookExecutionStatus.
  • The Events attribute defines an API-based event source that can trigger this function. It accepts requests on the /test path using an HTTP GET method.
'use strict';

const AWS = require('aws-sdk');
const codedeploy = new AWS.CodeDeploy({apiVersion: '2014-10-06'});
var lambda = new AWS.Lambda();

exports.handler = (event, context, callback) => {

	console.log("Entering PreTraffic Hook!");
	
	// Read the DeploymentId & LifecycleEventHookExecutionId from the event payload
    var deploymentId = event.DeploymentId;
	var lifecycleEventHookExecutionId = event.LifecycleEventHookExecutionId;

	var functionToTest = process.env.NewVersion;
	console.log("Testing new function version: " + functionToTest);

	// Perform validation of the newly deployed Lambda version
	var lambdaParams = {
		FunctionName: functionToTest,
		InvocationType: "RequestResponse"
	};

	var lambdaResult = "Failed";
	lambda.invoke(lambdaParams, function(err, data) {
		if (err){	// an error occurred
			console.log(err, err.stack);
			lambdaResult = "Failed";
		}
		else{	// successful response
			var result = JSON.parse(data.Payload);
			console.log("Result: " +  JSON.stringify(result));

			// Check the response for valid results
			// The response will be a JSON payload with statusCode and body properties. ie:
			// {
			//		"statusCode": 200,
			//		"body": 51
			// }
			if(result.body == 9){	
				lambdaResult = "Succeeded";
				console.log ("Validation testing succeeded!");
			}
			else{
				lambdaResult = "Failed";
				console.log ("Validation testing failed!");
			}

			// Complete the PreTraffic Hook by sending CodeDeploy the validation status
			var params = {
				deploymentId: deploymentId,
				lifecycleEventHookExecutionId: lifecycleEventHookExecutionId,
				status: lambdaResult // status can be 'Succeeded' or 'Failed'
			};
			
			// Pass AWS CodeDeploy the prepared validation test results.
			codedeploy.putLifecycleEventHookExecutionStatus(params, function(err, data) {
				if (err) {
					// Validation failed.
					console.log('CodeDeploy Status update failed');
					console.log(err, err.stack);
					callback("CodeDeploy Status update failed");
				} else {
					// Validation succeeded.
					console.log('Codedeploy status updated successfully');
					callback(null, 'Codedeploy status updated successfully');
				}
			});
		}  
	});
}

The hook is hardcoded to check that the number of S3 buckets returned is 9.

Review the key parts of the SAM template that defines preTrafficHook:

  • The Policies attribute specifies additional policy statements that SAM adds onto the automatically generated IAM role for this function. The first statement provides permissions to call the CodeDeploy PutLifecycleEventHookExecutionStatus API action. The second statement provides permissions to invoke the specific version of the returnS3Buckets function to test
  • This function has traffic shifting features disabled by setting the DeploymentPreference option to false.
  • The FunctionName attribute explicitly tells CloudFormation what to name the function. Otherwise, CloudFormation creates the function with the default naming convention: [stackName]-[FunctionName]-[uniqueID].  Name the function with the “CodeDeployHook_” prefix because the CodeDeployServiceRole role only allows InvokeFunction on functions named with that prefix.
  • Set the Timeout attribute to allow enough time to complete your validation tests.
  • Use an environment variable to inject the ARN of the newest deployed version of the returnS3Buckets function. The ARN allows the function to know the specific version to invoke and perform validation testing on.

Deploy the function

Your SAM template is all set and the code is written—you’re ready to deploy the function for the first time. Here’s how to do it via the SAM CLI. Replace “sam” with “cloudformation” to use CloudFormation instead.

First, package the function. This command returns a CloudFormation importable file, packaged.yaml.

sam package –template-file template.yaml –s3-bucket mybucket –output-template-file packaged.yaml

Now deploy everything:

sam deploy –template-file packaged.yaml –stack-name mySafeDeployStack –capabilities CAPABILITY_IAM

At this point, both Lambda functions have been deployed within the CloudFormation stack mySafeDeployStack. The returnS3Buckets has been deployed as Version 1:

SAM automatically created a few things, including the CodeDeploy application, with the deployment pattern that you specified (Linear10PercentEvery1Minute). There is currently one deployment group, with no action, because no deployments have occurred. SAM also created the IAM service role that this CodeDeploy application uses:

There is a single managed policy attached to this role, which allows CodeDeploy to invoke any Lambda function that begins with “CodeDeployHook_”.

An API has been set up called safeDeployStack. It targets your Lambda function with the /test resource using the GET method. When you test the endpoint, API Gateway executes the returnS3Buckets function and it returns the number of S3 buckets that you own. In this case, it’s 51.

Publish a new Lambda function version

Now implement the requirements change, which is to make returnS3Buckets count only buckets that begin with the letter “a”. The code now looks like the following (see returnS3BucketsNew.js in GitHub):

'use strict';

var AWS = require('aws-sdk');
var s3 = new AWS.S3();

exports.handler = (event, context, callback) => {
	console.log("I am here! " + context.functionName  +  ":"  +  context.functionVersion);

	s3.listBuckets(function (err, data){
		if(err){
			console.log(err, err.stack);
			callback(null, {
				statusCode: 500,
				body: "Failed!"
			});
		}
		else{
			var allBuckets = data.Buckets;

			console.log("Total buckets: " + allBuckets.length);
			//callback(null, allBuckets.length);

			//  New Code begins here
			var counter=0;
			for(var i  in allBuckets){
				if(allBuckets[i].Name[0] === "a")
					counter++;
			}
			console.log("Total buckets starting with a: " + counter);

			callback(null, {
				statusCode: 200,
				body: counter
			});
			
		}
	});	
}

Repackage and redeploy with the same two commands as earlier:

sam package –template-file template.yaml –s3-bucket mybucket –output-template-file packaged.yaml
	
sam deploy –template-file packaged.yaml –stack-name mySafeDeployStack –capabilities CAPABILITY_IAM

CloudFormation understands that this is a stack update instead of an entirely new stack. You can see that reflected in the CloudFormation console:

During the update, CloudFormation deploys the new Lambda function as version 2 and adds it to the “live” alias. There is no traffic routing there yet. CodeDeploy now takes over to begin the safe deployment process.

The first thing CodeDeploy does is invoke the preTrafficHook function. Verify that this happened by reviewing the Lambda logs and metrics:

The function should progress successfully, invoke Version 2 of returnS3Buckets, and finally invoke the CodeDeploy API with a success code. After this occurs, CodeDeploy begins the predefined rollout strategy. Open the CodeDeploy console to review the deployment progress (Linear10PercentEvery1Minute):

Verify the traffic shift

During the deployment, verify that the traffic shift has started to occur by running the test periodically. As the deployment shifts towards the new version, a larger percentage of the responses return 9 instead of 51. These numbers match the S3 buckets.

A minute later, you see 10% more traffic shifting to the new version. The whole process takes 10 minutes to complete. After completion, open the Lambda console and verify that the “live” alias now points to version 2:

After 10 minutes, the deployment is complete and CodeDeploy signals success to CloudFormation and completes the stack update.

Check the results

If you invoke the function alias manually, you see the results of the new implementation.

aws lambda invoke –function [lambda arn to live alias] out.txt

You can also execute the prod stage of your API and verify the results by issuing an HTTP GET to the invoke URL:

Summary

This post has shown you how you can safely automate your Lambda deployments using the Lambda traffic shifting feature. You used the Serverless Application Model (SAM) to define your Lambda functions and configured CodeDeploy to manage your deployment patterns. Finally, you used CloudFormation to automate the deployment and updates to your function and PreTraffic hook.

Now that you know all about this new feature, you’re ready to begin automating Lambda deployments with confidence that things will work as designed. I look forward to hearing about what you’ve built with the AWS Serverless Platform.

Using AWS Lambda and Amazon Comprehend for sentiment analysis

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/using-aws-lambda-and-amazon-comprehend-for-sentiment-analysis/

This post courtesy of Giedrius Praspaliauskas, AWS Solutions Architect

Even with best IVR systems, customers get frustrated. What if you knew that 10 callers in your Amazon Connect contact flow were likely to say “Agent!” in frustration in the next 30 seconds? Would you like to get to them before that happens? What if your bot was smart enough to admit, “I’m sorry this isn’t helping. Let me find someone for you.”?

In this post, I show you how to use AWS Lambda and Amazon Comprehend for sentiment analysis to make your Amazon Lex bots in Amazon Connect more sympathetic.

Setting up a Lambda function for sentiment analysis

There are multiple natural language and text processing frameworks or services available to use with Lambda, including but not limited to Amazon Comprehend, TextBlob, Pattern, and NLTK. Pick one based on the nature of your system:  the type of interaction, languages supported, and so on. For this post, I picked Amazon Comprehend, which uses natural language processing (NLP) to extract insights and relationships in text.

The walkthrough in this post is just an example. In a full-scale implementation, you would likely implement a more nuanced approach. For example, you could keep the overall sentiment score through the conversation and act only when it reaches a certain threshold. It is worth noting that this Lambda function is not called for missed utterances, so there may be a gap between what is being analyzed and what was actually said.

The Lambda function is straightforward. It analyses the input transcript field of the Amazon Lex event. Based on the overall sentiment value, it generates a response message with next step instructions. When the sentiment is neutral, positive, or mixed, the response leaves it to Amazon Lex to decide what the next steps should be. It adds to the response overall sentiment value as an additional session attribute, along with slots’ values received as an input.

When the overall sentiment is negative, the function returns the dialog action, pointing to an escalation intent (specified in the environment variable ESCALATION_INTENT_NAME) or returns the fulfillment closure action with a failure state when the intent is not specified. In addition to actions or intents, the function returns a message, or prompt, to be provided to the customer before taking the next step. Based on the returned action, Amazon Connect can select the appropriate next step in a contact flow.

For this walkthrough, you create a Lambda function using the AWS Management Console:

  1. Open the Lambda console.
  2. Choose Create Function.
  3. Choose Author from scratch (no blueprint).
  4. For Runtime, choose Python 3.6.
  5. For Role, choose Create a custom role. The custom execution role allows the function to detect sentiments, create a log group, stream log events, and store the log events.
  6. Enter the following values:
    • For Role Description, enter Lambda execution role permissions.
    • For IAM Role, choose Create an IAM role.
    • For Role Name, enter LexSentimentAnalysisLambdaRole.
    • For Policy, use the following policy:
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "arn:aws:logs:*:*:*"
        },
        {
            "Action": [
                "comprehend:DetectDominantLanguage",
                "comprehend:DetectSentiment"
            ],
            "Effect": "Allow",
            "Resource": "*"
        }
    ]
}
    1. Choose Create function.
    2. Copy/paste the following code to the editor window
import os, boto3

ESCALATION_INTENT_MESSAGE="Seems that you are having troubles with our service. Would you like to be transferred to the associate?"
FULFILMENT_CLOSURE_MESSAGE="Seems that you are having troubles with our service. Let me transfer you to the associate."

escalation_intent_name = os.getenv('ESACALATION_INTENT_NAME', None)

client = boto3.client('comprehend')

def lambda_handler(event, context):
    sentiment=client.detect_sentiment(Text=event['inputTranscript'],LanguageCode='en')['Sentiment']
    if sentiment=='NEGATIVE':
        if escalation_intent_name:
            result = {
                "sessionAttributes": {
                    "sentiment": sentiment
                    },
                    "dialogAction": {
                        "type": "ConfirmIntent", 
                        "message": {
                            "contentType": "PlainText", 
                            "content": ESCALATION_INTENT_MESSAGE
                        }, 
                    "intentName": escalation_intent_name
                    }
            }
        else:
            result = {
                "sessionAttributes": {
                    "sentiment": sentiment
                },
                "dialogAction": {
                    "type": "Close",
                    "fulfillmentState": "Failed",
                    "message": {
                            "contentType": "PlainText",
                            "content": FULFILMENT_CLOSURE_MESSAGE
                    }
                }
            }

    else:
        result ={
            "sessionAttributes": {
                "sentiment": sentiment
            },
            "dialogAction": {
                "type": "Delegate",
                "slots" : event["currentIntent"]["slots"]
            }
        }
    return result
  1. Below the code editor specify the environment variable ESCALATION_INTENT_NAME with a value of Escalate.

  1. Click on Save in the top right of the console.

Now you can test your function.

  1. Click Test at the top of the console.
  2. Configure a new test event using the following test event JSON:
{
  "messageVersion": "1.0",
  "invocationSource": "DialogCodeHook",
  "userId": "1234567890",
  "sessionAttributes": {},
  "bot": {
    "name": "BookSomething",
    "alias": "None",
    "version": "$LATEST"
  },
  "outputDialogMode": "Text",
  "currentIntent": {
    "name": "BookSomething",
    "slots": {
      "slot1": "None",
      "slot2": "None"
    },
    "confirmationStatus": "None"
  },
  "inputTranscript": "I want something"
}
  1. Click Create
  2. Click Test on the console

This message should return a response from Lambda with a sentiment session attribute of NEUTRAL.

However, if you change the input to “This is garbage!”, Lambda changes the dialog action to the escalation intent specified in the environment variable ESCALATION_INTENT_NAME.

Setting up Amazon Lex

Now that you have your Lambda function running, it is time to create the Amazon Lex bot. Use the BookTrip sample bot and call it BookSomething. The IAM role is automatically created on your behalf. Indicate that this bot is not subject to the COPPA, and choose Create. A few minutes later, the bot is ready.

Make the following changes to the default configuration of the bot:

  1. Add an intent with no associated slots. Name it Escalate.
  2. Specify the Lambda function for initialization and validation in the existing two intents (“BookCar” and “BookHotel”), at the same time giving Amazon Lex permission to invoke it.
  3. Leave the other configuration settings as they are and save the intents.

You are ready to build and publish this bot. Set a new alias, BookSomethingWithSentimentAnalysis. When the build finishes, test it.

As you see, sentiment analysis works!

Setting up Amazon Connect

Next, provision an Amazon Connect instance.

After the instance is created, you need to integrate the Amazon Lex bot created in the previous step. For more information, see the Amazon Lex section in the Configuring Your Amazon Connect Instance topic.  You may also want to look at the excellent post by Randall Hunt, New – Amazon Connect and Amazon Lex Integration.

Create a new contact flow, “Sentiment analysis walkthrough”:

  1. Log in into the Amazon Connect instance.
  2. Choose Create contact flow, Create transfer to agent flow.
  3. Add a Get customer input block, open the icon in the top left corner, and specify your Amazon Lex bot and its intents.
  4. Select the Text to speech audio prompt type and enter text for Amazon Connect to play at the beginning of the dialog.
  5. Choose Amazon Lex, enter your Amazon Lex bot name and the alias.
  6. Specify the intents to be used as dialog branches that a customer can choose: BookHotel, BookTrip, or Escalate.
  7. Add two Play prompt blocks and connect them to the customer input block.
    • If booking hotel or car intent is returned from the bot flow, play the corresponding prompt (“OK, will book it for you”) and initiate booking (in this walkthrough, just hang up after the prompt).
    • However, if escalation intent is returned (caused by the sentiment analysis results in the bot), play the prompt (“OK, transferring to an agent”) and initiate the transfer.
  8. Save and publish the contact flow.

As a result, you have a contact flow with a single customer input step and a text-to-speech prompt that uses the Amazon Lex bot. You expect one of the three intents returned:

Edit the phone number to associate the contact flow that you just created. It is now ready for testing. Call the phone number and check how your contact flow works.

Cleanup

Don’t forget to delete all the resources created during this walkthrough to avoid incurring any more costs:

  • Amazon Connect instance
  • Amazon Lex bot
  • Lambda function
  • IAM role LexSentimentAnalysisLambdaRole

Summary

In this walkthrough, you implemented sentiment analysis with a Lambda function. The function can be integrated into Amazon Lex and, as a result, into Amazon Connect. This approach gives you the flexibility to analyze user input and then act. You may find the following potential use cases of this approach to be of interest:

  • Extend the Lambda function to identify “hot” topics in the user input even if the sentiment is not negative and take action proactively. For example, switch to an escalation intent if a user mentioned “where is my order,” which may signal potential frustration.
  • Use Amazon Connect Streams to provide agent sentiment analysis results along with call transfer. Enable service tailored towards particular customer needs and sentiments.
  • Route calls to agents based on both skill set and sentiment.
  • Prioritize calls based on sentiment using multiple Amazon Connect queues instead of transferring directly to an agent.
  • Monitor quality and flag for review contact flows that result in high overall negative sentiment.
  • Implement sentiment and AI/ML based call analysis, such as a real-time recommendation engine. For more details, see Machine Learning on AWS.

If you have questions or suggestions, please comment below.

Node.js 8.10 runtime now available in AWS Lambda

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/node-js-8-10-runtime-now-available-in-aws-lambda/

This post courtesy of Ed Lima, AWS Solutions Architect

We are excited to announce that you can now develop your AWS Lambda functions using the Node.js 8.10 runtime, which is the current Long Term Support (LTS) version of Node.js. Start using this new version today by specifying a runtime parameter value of nodejs8.10 when creating or updating functions.

Supporting async/await

The Lambda programming model for Node.js 8.10 now supports defining a function handler using the async/await pattern.

Asynchronous or non-blocking calls are an inherent and important part of applications, as user and human interfaces are asynchronous by nature. If you decide to have a coffee with a friend, you usually order the coffee then start or continue a conversation with your friend while the coffee is getting ready. You don’t wait for the coffee to be ready before you start talking. These activities are asynchronous, because you can start one and then move to the next without waiting for completion. Otherwise, you’d delay (or block) the start of the next activity.

Asynchronous calls used to be handled in Node.js using callbacks. That presented problems when they were nested within other callbacks in multiple levels, making the code difficult to maintain and understand.

Promises were implemented to try to solve issues caused by “callback hell.” They allow asynchronous operations to call their own methods and handle what happens when a call is successful or when it fails. As your requirements become more complicated, even promises become harder to work with and may still end up complicating your code.

Async/await is the new way of handling asynchronous operations in Node.js, and makes for simpler, easier, and cleaner code for non-blocking calls. It still uses promises but a callback is returned directly from the asynchronous function, just as if it were a synchronous blocking function.

Take for instance the following Lambda function to get the current account settings, using the Node.js 6.10 runtime:

let AWS = require('aws-sdk');
let lambda = new AWS.Lambda();

exports.handler = (event, context, callback) => {
    let getAccountSettingsPromise = lambda.getAccountSettings().promise();
    getAccountSettingsPromise.then(
        (data) => {
            callback(null, data);
        },
        (err) => {
            console.log(err);
            callback(err);
        }
    );
};

With the new Node.js 8.10 runtime, there are new handler types that can be declared with the “async” keyword or can return a promise directly.

This is how the same function looks like using async/await with Node.js 8.10:

let AWS = require('aws-sdk');
let lambda = new AWS.Lambda();

exports.handler = async (event) => {
    return await lambda.getAccountSettings().promise() ;
};

Alternatively, you could have the handler return a promise directly:

let AWS = require('aws-sdk');
let lambda = new AWS.Lambda();

exports.handler = (event) => {
    return new Promise((resolve, reject) => {
        lambda.getAccountSettings(event)
        .then((data) => {
            resolve data;
        })
        .catch(reject);
     });
};

The new handler types are alternatives to the callback pattern, which is still fully supported.

All three functions return the same results. However, in the new runtime with async/await, all callbacks in the code are gone, which makes it easier to read. This is especially true for those less familiar with promises.

{
    "AccountLimit":{
        "TotalCodeSize":80530636800,
        "CodeSizeUnzipped":262144000,
        "CodeSizeZipped":52428800, 
        "ConcurrentExecutions":1000,
        "UnreservedConcurrentExecutions":1000
    },
    "AccountUsage":{
        "TotalCodeSize":52234461,
        "FunctionCount":53
    }
}

Another great advantage of async/await is better error handling. You can use a try/catch block inside the scope of an async function. Even though the function awaits an asynchronous operation, any errors end up in the catch block.

You can improve your previous Node.js 8.10 function with this trusted try/catch error handling pattern:

let AWS = require('aws-sdk');
let lambda = new AWS.Lambda();
let data;

exports.handler = async (event) => {
    try {
        data = await lambda.getAccountSettings().promise();
    }
    catch (err) {
        console.log(err);
        return err;
    }
    return data;
};

While you now have a similar number of lines in both runtimes, the code is cleaner and more readable with async/await. It makes the asynchronous calls look more synchronous. However, it is important to notice that the code is still executed the same way as if it were using a callback or promise-based API.

Backward compatibility

You may port your existing Node.js 4.3 and 6.10 functions over to Node.js 8.10 by updating the runtime. Node.js 8.10 does include numerous breaking changes from previous Node versions.

Make sure to review the API changes between Node.js 4.3, 6.10, and Node.js 8.10 to see if there are other changes that might affect your code. We recommend testing that your Lambda function passes internal validation for its behavior when upgrading to the new runtime version.

You can use Lambda versions/aliases to safely test that your function runs as expected on Node 8.10, before routing production traffic to it.

New node features

You can now get better performance when compared to the previous LTS version 6.x (up to 20%). The new V8 6.0 engine comes with Turbofan and the Ignition pipeline, which leads to lower memory consumption and faster startup time across Node.js applications.

HTTP/2, which is subject to future changes, allows developers to use the new protocol to speed application development and undo many of HTTP/1.1 workarounds to make applications faster, simpler, and more powerful.

For more information, see the AWS Lambda Developer Guide.

Hope you enjoy and… go build with Node.js 8.10!

Tracing Stolen Bitcoin

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/03/tracing_stolen_.html

Ross Anderson has a really interesting paper on tracing stolen bitcoin. From a blog post:

Previous attempts to track tainted coins had used either the “poison” or the “haircut” method. Suppose I open a new address and pay into it three stolen bitcoin followed by seven freshly-mined ones. Then under poison, the output is ten stolen bitcoin, while under haircut it’s ten bitcoin that are marked 30% stolen. After thousands of blocks, poison tainting will blacklist millions of addresses, while with haircut the taint gets diffused, so neither is very effective at tracking stolen property. Bitcoin due-diligence services supplant haircut taint tracking with AI/ML, but the results are still not satisfactory.

We discovered that, back in 1816, the High Court had to tackle this problem in Clayton’s case, which involved the assets and liabilities of a bank that had gone bust. The court ruled that money must be tracked through accounts on the basis of first-in, first out (FIFO); the first penny into an account goes to satisfy the first withdrawal, and so on.

Ilia Shumailov has written software that applies FIFO tainting to the blockchain and the results are impressive, with a massive improvement in precision. What’s more, FIFO taint tracking is lossless, unlike haircut; so in addition to tracking a stolen coin forward to find where it’s gone, you can start with any UTXO and trace it backwards to see its entire ancestry. It’s not just good law; it’s good computer science too.

Appeals Court Overturns Google’s Fair Use Victory For Java APIs (Techdirt)

Post Syndicated from corbet original https://lwn.net/Articles/750228/rss

Techdirt reports
that the US Court of Appeals for the Federal Circuit (CAFC) has resurrected
Oracle’s copyright claim against Google for its use of the Java APIs in
Android. “Honestly, the most concerning part of the whole thing is
how much of a mess CAFC has made of the whole process. The court ruled
correctly originally that APIs are not subject to copyright. CAFC threw
that out and ordered the court to have a jury determine the fair use
question. The jury found it to be fair use, and even though CAFC had
ordered the issue be heard by a jury, it now says ‘meh, we disagree with
the jury.’ That’s… bizarre.

ЕСПЧ: прилагане на решенията

Post Syndicated from nellyo original https://nellyo.wordpress.com/2018/03/14/%D0%B5%D1%81%D0%BF%D1%87-%D0%BF%D1%80%D0%B8%D0%BB%D0%B0%D0%B3%D0%B0%D0%BD%D0%B5-%D0%BD%D0%B0-%D1%80%D0%B5%D1%88%D0%B5%D0%BD%D0%B8%D1%8F%D1%82%D0%B0/

Съд за правата на човека:

10-те държави с най-голям брой на  решенията,  които все още очакват прилагане: Италия, Русия, Турция, Украйна, Румъния, Унгария, Гърция, България, Молдова и Полша.

Източник:

The implementation of judgments of the European Court of Human Rights (2018)

 

Raspberry Pi 3 Model B+ on sale now at $35

Post Syndicated from Eben Upton original https://www.raspberrypi.org/blog/raspberry-pi-3-model-bplus-sale-now-35/

Here’s a long post. We think you’ll find it interesting. If you don’t have time to read it all, we recommend you watch this video, which will fill you in with everything you need, and then head straight to the product page to fill yer boots. (We recommend the video anyway, even if you do have time for a long read. ‘Cos it’s fab.)

A BRAND-NEW PI FOR π DAY

Raspberry Pi 3 Model B+ is now on sale now for $35, featuring: – A 1.4GHz 64-bit quad-core ARM Cortex-A53 CPU – Dual-band 802.11ac wireless LAN and Bluetooth 4.2 – Faster Ethernet (Gigabit Ethernet over USB 2.0) – Power-over-Ethernet support (with separate PoE HAT) – Improved PXE network and USB mass-storage booting – Improved thermal management Alongside a 200MHz increase in peak CPU clock frequency, we have roughly three times the wired and wireless network throughput, and the ability to sustain high performance for much longer periods.

If you’ve been a Raspberry Pi watcher for a while now, you’ll have a bit of a feel for how we update our products. Just over two years ago, we released Raspberry Pi 3 Model B. This was our first 64-bit product, and our first product to feature integrated wireless connectivity. Since then, we’ve sold over nine million Raspberry Pi 3 units (we’ve sold 19 million Raspberry Pis in total), which have been put to work in schools, homes, offices and factories all over the globe.

Those Raspberry Pi watchers will know that we have a history of releasing improved versions of our products a couple of years into their lives. The first example was Raspberry Pi 1 Model B+, which added two additional USB ports, introduced our current form factor, and rolled up a variety of other feedback from the community. Raspberry Pi 2 didn’t get this treatment, of course, as it was superseded after only one year; but it feels like it’s high time that Raspberry Pi 3 received the “plus” treatment.

So, without further ado, Raspberry Pi 3 Model B+ is now on sale for $35 (the same price as the existing Raspberry Pi 3 Model B), featuring:

  • A 1.4GHz 64-bit quad-core ARM Cortex-A53 CPU
  • Dual-band 802.11ac wireless LAN and Bluetooth 4.2
  • Faster Ethernet (Gigabit Ethernet over USB 2.0)
  • Power-over-Ethernet support (with separate PoE HAT)
  • Improved PXE network and USB mass-storage booting
  • Improved thermal management

Alongside a 200MHz increase in peak CPU clock frequency, we have roughly three times the wired and wireless network throughput, and the ability to sustain high performance for much longer periods.

Behold the shiny

Raspberry Pi 3B+ is available to buy today from our network of Approved Resellers.

New features, new chips

Roger Thornton did the design work on this revision of the Raspberry Pi. Here, he and I have a chat about what’s new.

Introducing the Raspberry Pi 3 Model B+

Raspberry Pi 3 Model B+ is now on sale now for $35, featuring: – A 1.4GHz 64-bit quad-core ARM Cortex-A53 CPU – Dual-band 802.11ac wireless LAN and Bluetooth 4.2 – Faster Ethernet (Gigabit Ethernet over USB 2.0) – Power-over-Ethernet support (with separate PoE HAT) – Improved PXE network and USB mass-storage booting – Improved thermal management Alongside a 200MHz increase in peak CPU clock frequency, we have roughly three times the wired and wireless network throughput, and the ability to sustain high performance for much longer periods.

The new product is built around BCM2837B0, an updated version of the 64-bit Broadcom application processor used in Raspberry Pi 3B, which incorporates power integrity optimisations, and a heat spreader (that’s the shiny metal bit you can see in the photos). Together these allow us to reach higher clock frequencies (or to run at lower voltages to reduce power consumption), and to more accurately monitor and control the temperature of the chip.

Dual-band wireless LAN and Bluetooth are provided by the Cypress CYW43455 “combo” chip, connected to a Proant PCB antenna similar to the one used on Raspberry Pi Zero W. Compared to its predecessor, Raspberry Pi 3B+ delivers somewhat better performance in the 2.4GHz band, and far better performance in the 5GHz band, as demonstrated by these iperf results from LibreELEC developer Milhouse.

Tx bandwidth (Mb/s)Rx bandwidth (Mb/s)
Raspberry Pi 3B35.735.6
Raspberry Pi 3B+ (2.4GHz)46.746.3
Raspberry Pi 3B+ (5GHz)102102

The wireless circuitry is encapsulated under a metal shield, rather fetchingly embossed with our logo. This has allowed us to certify the entire board as a radio module under FCC rules, which in turn will significantly reduce the cost of conformance testing Raspberry Pi-based products.

We’ll be teaching metalwork next.

Previous Raspberry Pi devices have used the LAN951x family of chips, which combine a USB hub and 10/100 Ethernet controller. For Raspberry Pi 3B+, Microchip have supported us with an upgraded version, LAN7515, which supports Gigabit Ethernet. While the USB 2.0 connection to the application processor limits the available bandwidth, we still see roughly a threefold increase in throughput compared to Raspberry Pi 3B. Again, here are some typical iperf results.

Tx bandwidth (Mb/s)Rx bandwidth (Mb/s)
Raspberry Pi 3B94.195.5
Raspberry Pi 3B+315315

We use a magjack that supports Power over Ethernet (PoE), and bring the relevant signals to a new 4-pin header. We will shortly launch a PoE HAT which can generate the 5V necessary to power the Raspberry Pi from the 48V PoE supply.

There… are… four… pins!

Coming soon to a Raspberry Pi 3B+ near you

Raspberry Pi 3B was our first product to support PXE Ethernet boot. Testing it in the wild shook out a number of compatibility issues with particular switches and traffic environments. Gordon has rolled up fixes for all known issues into the BCM2837B0 boot ROM, and PXE boot is now enabled by default.

Clocking, voltages and thermals

The improved power integrity of the BCM2837B0 package, and the improved regulation accuracy of our new MaxLinear MxL7704 power management IC, have allowed us to tune our clocking and voltage rules for both better peak performance and longer-duration sustained performance.

Below 70°C, we use the improvements to increase the core frequency to 1.4GHz. Above 70°C, we drop to 1.2GHz, and use the improvements to decrease the core voltage, increasing the period of time before we reach our 80°C thermal throttle; the reduction in power consumption is such that many use cases will never reach the throttle. Like a modern smartphone, we treat the thermal mass of the device as a resource, to be spent carefully with the goal of optimising user experience.

This graph, courtesy of Gareth Halfacree, demonstrates that Raspberry Pi 3B+ runs faster and at a lower temperature for the duration of an eight‑minute quad‑core Sysbench CPU test.

Note that Raspberry Pi 3B+ does consume substantially more power than its predecessor. We strongly encourage you to use a high-quality 2.5A power supply, such as the official Raspberry Pi Universal Power Supply.

FAQs

We’ll keep updating this list over the next couple of days, but here are a few to get you started.

Are you discontinuing earlier Raspberry Pi models?

No. We have a lot of industrial customers who will want to stick with the existing products for the time being. We’ll keep building these models for as long as there’s demand. Raspberry Pi 1B+, Raspberry Pi 2B, and Raspberry Pi 3B will continue to sell for $25, $35, and $35 respectively.

What about Model A+?

Raspberry Pi 1A+ continues to be the $20 entry-level “big” Raspberry Pi for the time being. We are considering the possibility of producing a Raspberry Pi 3A+ in due course.

What about the Compute Module?

CM1, CM3 and CM3L will continue to be available. We may offer versions of CM3 and CM3L with BCM2837B0 in due course, depending on customer demand.

Are you still using VideoCore?

Yes. VideoCore IV 3D is the only publicly-documented 3D graphics core for ARM‑based SoCs, and we want to make Raspberry Pi more open over time, not less.

Credits

A project like this requires a vast amount of focused work from a large team over an extended period. Particular credit is due to Roger Thornton, who designed the board and ran the exhaustive (and exhausting) RF compliance campaign, and to the team at the Sony UK Technology Centre in Pencoed, South Wales. A partial list of others who made major direct contributions to the BCM2837B0 chip program, CYW43455 integration, LAN7515 and MxL7704 developments, and Raspberry Pi 3B+ itself follows:

James Adams, David Armour, Jonathan Bell, Maria Blazquez, Jamie Brogan-Shaw, Mike Buffham, Rob Campling, Cindy Cao, Victor Carmon, KK Chan, Nick Chase, Nigel Cheetham, Scott Clark, Nigel Clift, Dominic Cobley, Peter Coyle, John Cronk, Di Dai, Kurt Dennis, David Doyle, Andrew Edwards, Phil Elwell, John Ferdinand, Doug Freegard, Ian Furlong, Shawn Guo, Philip Harrison, Jason Hicks, Stefan Ho, Andrew Hoare, Gordon Hollingworth, Tuomas Hollman, EikPei Hu, James Hughes, Andy Hulbert, Anand Jain, David John, Prasanna Kerekoppa, Shaik Labeeb, Trevor Latham, Steve Le, David Lee, David Lewsey, Sherman Li, Xizhe Li, Simon Long, Fu Luo Larson, Juan Martinez, Sandhya Menon, Ben Mercer, James Mills, Max Passell, Mark Perry, Eric Phiri, Ashwin Rao, Justin Rees, James Reilly, Matt Rowley, Akshaye Sama, Ian Saturley, Serge Schneider, Manuel Sedlmair, Shawn Shadburn, Veeresh Shivashimper, Graham Smith, Ben Stephens, Mike Stimson, Yuree Tchong, Stuart Thomson, John Wadsworth, Ian Watch, Sarah Williams, Jason Zhu.

If you’re not on this list and think you should be, please let me know, and accept my apologies.

The post Raspberry Pi 3 Model B+ on sale now at $35 appeared first on Raspberry Pi.

Welte: Report from the Geniatech vs. McHardy GPL violation court hearing

Post Syndicated from corbet original https://lwn.net/Articles/748761/rss

Harald Welte attended a hearing in one of the Patrick McHardy GPL cases and
wrote up
what he saw
.

I’m not arguing for a “too soft” approach. It’s
almost 15 years since the first court cases on license violations on
(embedded) Linux, and the fact that the problem still exists today clearly
shows the industry is very far from having solved a seemingly rather simple
problem.

On the other hand, such activities must always be oriented to compliance,
and compliance only. Collecting huge amounts of contractual penalties is
questionable. And if it was necessary to collect such huge amounts to
motivate large corporations to be compliant, then this must be done in the
open, with the community knowing about it, and the proceeds of such
contractual penalties must be donated to free software related entities to
prove that personal financial gain is not a motivation.

Setting up bug bounties for success

Post Syndicated from Michal Zalewski original https://lcamtuf.blogspot.com/2018/03/setting-up-bug-bounties-for-success.html

Bug bounties end up in the news with some regularity, usually for the wrong reasons. I’ve been itching to write
about that for a while – but instead of dwelling on the mistakes of the bygone days, I figured it may be better to
talk about some of the ways to get vulnerability rewards right.

What do you get out of bug bounties?

There’s plenty of differing views, but I like to think of such programs
simply as a bid on researchers’ time. In the most basic sense, you get three benefits:

  • Improved ability to detect bugs in production before they become major incidents.
  • A comparatively unbiased feedback loop to help you prioritize and measure other security work.
  • A robust talent pipeline for when you need to hire.

What bug bounties don’t offer?

You don’t get anything resembling a comprehensive security program or a systematic assessment of your platforms.
Researchers end up looking for bugs that offer favorable effort-to-payoff ratios for their skills and given the
very imperfect information they have about your enterprise. In other words, you may end up with a hundred
people looking for XSS and just one person looking for RCE.

Your reward structure can steer them toward the targets and bugs you care about, but it’s difficult to fully
eliminate this inherent skew. There’s only so far you can jack up your top-tier rewards, and only so far you can
go lowering the bottom-tier ones.

Don’t you have to outcompete the black market to get all the “good” bugs?

There is a free market price discovery component to it all: if you’re not getting the engagement you
were hoping for, you should probably consider paying more.

That said, there are going to be researchers who’d rather hurt you than work for you, no matter how much you pay;
you don’t have to win them over, and you don’t have to outspend every authoritarian government or
every crime syndicate. A bug bounty is effective simply if it attracts enough eyeballs to make bugs statistically
harder to find, and reduces the useful lifespan of any zero-days in black market trade. Plus, most
researchers don’t want their work to be used to crack down on dissidents in Egypt or Vietnam.

Another factor is that you’re paying for different things: a black market buyer probably wants a reliable exploit
capable of delivering payloads, and then demands silence for months or years to come; a vendor-run
bug bounty program is usually perfectly happy with a reproducible crash and doesn’t mind a researcher blogging
about their work.

In fact, while money is important, you will probably find out that it’s not enough to retain your top talent;
many folks want bug bounties to be more than a business transaction, and find a lot of value in having a close
relationship with your security team, comparing notes, and growing together. Fostering that partnership can
be more important than adding another $10,000 to your top reward.

How do I prevent it all from going horribly wrong?

Bug bounties are an unfamiliar beast to most lawyers and PR folks, so it’s a natural to be wary and try to plan
for every eventuality with pages and pages of impenetrable rules and fine-print legalese.

This is generally unnecessary: there is a strong self-selection bias, and almost every participant in a
vulnerability reward program will be coming to you in good faith. The more friendly, forthcoming, and
approachable you seem, and the more you treat them like peers, the more likely it is for your relationship to stay
positive. On the flip side, there is no faster way to make enemies than to make a security researcher feel that they
are now talking to a lawyer or to the PR dept.

Most people have strong opinions on disclosure policies; instead of imposing your own views, strive to patch reported bugs
reasonably quickly, and almost every reporter will play along. Demand researchers to cancel conference appearances,
take down blog posts, or sign NDAs, and you will sooner or later end up in the news.

But what if that’s not enough?

As with any business endeavor, mistakes will happen; total risk avoidance is seldom the answer. Learn to sincerely
apologize for mishaps; it’s not a sign of weakness to say “sorry, we messed up”. And you will almost certainly not end
up in the courtroom for doing so.

It’s good to foster a healthy and productive relationship with the community, so that they come to your defense when
something goes wrong. Encouraging people to disclose bugs and talk about their experiences is one way of accomplishing that.

What about extortion?

You should structure your program to naturally discourage bad behavior and make it stand out like a sore thumb.
Require bona fide reports with complete technical details before any reward decision is made by a panel of named peers;
and make it clear that you never demand non-disclosure as a condition of getting a reward.

To avoid researchers accidentally putting themselves in awkward situations, have clear rules around data exfiltration
and lateral movement: assure them that you will always pay based on the worst-case impact of their findings; in exchange,
ask them to stop as soon as they get a shell and never access any data that isn’t their own.

So… are there any downsides?

Yep. Other than souring up your relationship with the community if you implement your program wrong, the other consideration
is that bug bounties tend to generate a lot of noise from well-meaning but less-skilled researchers.

When this happens, do not get frustrated and do not penalize such participants; instead, help them grow. Consider
publishing educational articles, giving advice on how to investigate and structure reports, or
offering free workshops every now and then.

The other downside is cost; although bug bounties tend to offer far more bang for your buck than your average penetration
test, they are more random. The annual expenses tend to be fairly predictable, but there is always
some possibility of having to pay multiple top-tier rewards in rapid succession. This is the kind of uncertainty that
many mid-level budget planners react badly to.

Finally, you need to be able to fix the bugs you receive. It would be nuts to prefer to not know about the
vulnerabilities in the first place – but once you invite the research, the clock starts ticking and you need to
ship fixes reasonably fast.

So… should I try it?

There are folks who enthusiastically advocate for bug bounties in every conceivable situation, and people who dislike them
with fierce passion; both sentiments are usually strongly correlated with the line of business they are in.

In reality, bug bounties are not a cure-all, and there are some ways to make them ineffectual or even dangerous.
But they are not as risky or expensive as most people suspect, and when done right, they can actually be fun for your
team, too. You won’t know for sure until you try.

qrocodile: the kid-friendly Sonos system

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/qrocodile-kid-friendly-sonos-system/

Chris Campbell’s qrocodile uses a Raspberry Pi, a camera, and QR codes to allow Chris’s children to take full control of the Sonos home sound system. And we love it!

qrocodile

Introducing qrocodile, a kid-friendly system for controlling your Sonos with QR codes. Source code is available at: https://github.com/chrispcampbell/qrocodile Learn more at: http://labonnesoupe.org https://twitter.com/chrscmpbll

Sonos

SONOS is SONOS backwards. It’s also SONOS upside down, and SONOS upside down and backwards. I just learnt that this means SONOS is an ambigram. Hurray for learning!

Sonos (the product, not the ambigram) is a multi-room speaker system controlled by an app. Speakers in different rooms can play different tracks or join forces to play one track for a smooth musical atmosphere throughout your home.

sonos raspberry pi

If you have a Sonos system in your home, I would highly recommend accessing to it from outside your home and set it to play the Imperial March as you walk through the front door. Why wouldn’t you?

qrocodile

One day, Chris’s young children wanted to play an album while eating dinner. By this one request, he was inspired to create qrocodile, a musical jukebox enabling his children to control the songs Sonos plays, and where it plays them, via QR codes.

It all started one night at the dinner table over winter break. The kids wanted to put an album on the turntable (hooked up to the line-in on a Sonos PLAY:5 in the dining room). They’re perfectly capable of putting vinyl on the turntable all by themselves, but using the Sonos app to switch over to play from the line-in is a different story.

The QR codes represent commands (such as Play in the living room, Use the turntable, or Build a song list) and artists (such as my current musical crush Courtney Barnett or the Ramones).

qrocodile raspberry Pi

A camera attached to a Raspberry Pi 3 feeds the Pi the QR code that’s presented, and the Pi runs a script that recognises the code and sends instructions to Sonos accordingly.


Chris used a costum version of the Sonos HTTP API created by Jimmy Shimizu to gain access to Sonos from his Raspberry Pi. To build the QR codes, he wrote a script that utilises the Spotify API via the Spotipy library.

His children are now able to present recognisable album art to the camera in order to play their desired track.

It’s been interesting seeing the kids putting the thing through its paces during their frequent “dance parties”, queuing up their favorite songs and uncovering new ones. I really like that they can use tangible objects to discover music in much the same way I did when I was their age, looking through my parents records, seeing which ones had interesting artwork or reading the song titles on the back, listening and exploring.

Chris has provided all the scripts for the project, along with a tutorial of how to set it up, on his GitHub — have a look if you want to recreate it or learn more about his code. Also check out Chris’ website for more on qrocodile and to see some of his other creations.

The post qrocodile: the kid-friendly Sonos system appeared first on Raspberry Pi.