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Workshop exercise materials for re:Invent 2017 - SID 341: Using AWS CloudTrail Logs for Scalable, Automated Anomaly Detection
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README.md

SID 341: Using AWS CloudTrail Logs for Scalable, Automated Anomaly Detection

In this README you will find instructions and pointers to the resources used for the workshop exercises. In this workshop, there are two exercises:

  1. Examining CloudTrail logs
  2. Automated detection

After the setup steps below, there are instructions provided for all of the hands-on exercises, clean-up instructions to tear down the CloudFormation stack, and following that a full walkthrough guide on how to complete the exercises.

What's in here?

This repository contains the following files that will be used for this workshop:

  • README.md - This README file
  • cloudformation.yaml - The CloudFormation template to deploy the stack of resources
  • cloudtrail_analyzer.py - Source code for the Lambda function to analyze CloudTrail logs
    • This code is just for reference since it will also be present in the inline editor in the Lambda console after the CloudFormation stack is deployed
  • teardown.sh - Bash script that deletes the CloudFormation stack
    • Attempting to delete the stack from the console will fail due to the created S3 buckets having contents that need to be deleted first, so this script handles that gracefully. In the Cleaning up section later, there are also manual clean-up steps included in case you cannot run this script.

Initial setup

Prerequisites

Before getting started, you will need the following:

  • AWS account
  • Modern, graphical web browser (sorry Lynx users!)
  • IAM user with administrator access to the account
  • AWS CLI - https://aws.amazon.com/cli/
    • Only needed for the teardown.sh script

Deploying the template

The CloudFormation template creates 2 sets of resources for the following purposes:

  1. Analysis of CloudTrail logs using an AWS Lambda function
  2. Activity generation to produce CloudTrail logs for analysis

First, log in to your AWS account using the IAM user with administrator access.

For this workshop, we will be working within the Canada Central (ca-central-1) region. To switch regions, click the region dropdown in the top right of the window and select Canada (Central).

To easily deploy the CloudFormation stack in the Canada (Central) region, please browse to the following stack launch URL:

http://amzn.to/sid341cfn

That stack launch URL uses a copy of the cloudformation.yaml template that is contained in an S3 bucket, which is the same as the one contained in this code repository.

  1. On the Select Template page, note that the template location where it says "Specify an Amazon S3 template URL" is prepopulated with the S3 URL to the template. Click Next.
  2. On the Specify Details page, note that the stack name is prepopulated as "ReInvent2017-SID341", but you may change it if desired. If you'd like to receive alarm notifications via email later when we add support for alarming on CloudTrail-based detections, please fill in the NotificationEmailAddress parameter with your email address. Please note that if specifying a notification email address, you will receive a subscription confirmation email shortly after the stack creation completes in which you must click a link to confirm the subscription. Click Next.
  3. On the Options screen, click Next.
  4. On the Review page, review and confirm the settings. Be sure to check the box acknowledging that the template will create resources.
  5. Click Create to deploy the stack. You can view the status of the stack in the AWS CloudFormation console in the Status column. You should see a status of CREATE_COMPLETE in roughly five minutes.

Exercise 1: Examining CloudTrail logs

In this exercise, you will examine CloudTrail logs in your account, which will include generated activity from the CloudFormation stack you deployed earlier. The goal of this exercise is to familiarize with the structure of the CloudTrail logs, their format, and content.

  1. Go to the CloudTrail console, then click on Event history on the left menu.
  2. Click to expand several different types of events and observe the information presented.
  3. With several different events, click the View event button while they are expanded to view their raw JSON records. Note the userIdentity block, along with some of the more interesting fields like sourceIPAddress, eventSource, and eventName. Depending on the event, you may also see some requestParameters and responseElements present.

Exercise 2: Automated detection

In this exercise, you will build a simple CloudTrail log analyzer and detection engine using a Python-based AWS Lambda function.

Some core functionality of the Lambda function has already been provided for you and takes care of the following:

  • Receiving and handling incoming notification messages when a new CloudTrail log file gets created in the S3 bucket (the handler entry point function)
  • Fetching, gunzipping, and loading each CloudTrail log file, and converting the JSON to a Python dictionary that allows straightforward referencing of fields in the event records (the get_log_file_location and get_records functions).
  • Iterating over all of the records in each log file and passing each record to analysis functions (for loops in the handler that pass individual records to each analysis function in the analysis_functions list).

Getting started

Here are steps you can follow to begin the exercise:

  1. Browse to the AWS Lambda console, double checking that you are in region Canada (Central), or ca-central-1 as shown in the URL.
  2. Click on the function whose name begins with "ReInvent2017-SID341-AnalysisLambdaFunc". This is the CloudTrail Analysis Lambda function.
  3. You should be on the Configuration tab. Scroll down and under Function code you will see the code for the Lambda function in the inline, browser-based editor. Skim through the code to become familiar with what it does and how it is structured. The code has copious comments that will help guide you through what each part does.
  4. Whenever you make a code change to the Lambda function, you will need to click the Save button at the top (just Save, not Save and test) to save your changes before they will take effect. The new code will then be in effect on the next invocation (i.e., when the next CloudTrail log file gets created).

Look at each of the functions contained in the analysis_functions tuple. Each of these functions gets passed every CloudTrail record.

The print_short_record analysis function is already defined. All it does is print out a shortened form of each CloudTrail record by reading certain fields in the CloudTrail record. Observe how these fields are accessed since you will need to do something similar in the other analysis functions.

The CloudTrail User Guide has a reference on log events that explains in detail what each of the fields in a CloudTrail record mean. You will likely find this to be helpful as you start to analyze the events:

http://docs.aws.amazon.com/awscloudtrail/latest/userguide/cloudtrail-event-reference.html

To see what the abbreviated CloudTrail records being printed by print_short_record look like, go to the Monitoring tab for the Lambda function and click on View logs in CloudWatch. Once in CloudWatch, click on the Log Stream and you will see the output from every invocation of the Lambda function.

Note that the sourceIPAddress value for actions performed by the CloudFormation template is "cloudformation.amazonaws.com". Also, notice that some actions have a region of "us-east-1" rather than "ca-central-1". This is because some services, such as IAM (iam.amazonaws.com), are global services and report region as "us-east-1" (trivia: that was the first AWS region!).

Notes on testing the Lambda function

The Analysis Lambda function will be invoked about every 5 minutes when new CloudTrail log files get created, so you don't need to set up a test event if you're okay with waiting until the next invocation happens automatically.

However, if you'd like to set up a test event to be able to Save and Test (or Test) the function as you make changes and have it run immediately, you can use the following template for a simplified version of the S3 Put test event to do so:

{
  "Records": [
    {
      "s3": {
        "bucket": {
          "name": "CLOUDTRAIL_BUCKET_NAME"
        },
        "object": {
          "key": "CLOUDTRAIL_LOG_FILE_PATH"
        }
      }
    }
  ]
}
  1. At the Analysis Lambda function, click the dropdown where it says Select a test event then click Configure test event.
  2. Select the Event template for "S3 Put" and fill in the Event name as "S3PutTest".
  3. Paste the JSON blurb above for the sample test event into the editor.
  4. Open a new tab and go to the S3 console. Find the bucket starting with "reinvent2017-sid341-cloudtrailbucket". Replace "CLOUDTRAIL_BUCKET_NAME" in the sample event with the name of this CloudTrail bucket.
  5. Go back to the S3 console tab and click on the bucket whose name starts with "reinvent2017-sid341-cloudtrailbucket". Find any CloudTrail log file in this bucket by navigating a path like the following (some values will be different): AWSLogs/012345678900/CloudTrail/ca-central-1/2017/11/29/.
  6. Click on a log file. On the next screen there will be a button labelled Copy path. Clicking that will copy the full path to this log file. Replace "CLOUDTRAIL_LOG_FILE_PATH" in the sample event with this path.
  7. Click Create to create the test event.

Above the Analysis Lambda function you should now see the test event selected. By clicking the Test button your function will be immediately invoked with that event, which will load and analyze the same CloudTrail log file every time it is run.

Phase 1: Deleting logs

The deletion of logs is an action that should normally not occur in most accounts, and may indicate an attacker trying to cover tracks.

In this exercise, we will focus on API calls that delete logs in CloudWatch Logs or CloudTrail. You need to implement code for the deleting_logs function to check for those API calls by looking for API events whose name starts with "Delete". For the purposes of this exercise, you can focus this check on just CloudWatch Logs (logs.amazonaws.com) and CloudTrail (cloudtrail.amazonaws.com).

Use what you've learned about looking at CloudTrail records so far to identify the fields you will need to use, and borrow code patterns from print_short_record as needed.

When a matching record is found, print it out using print_short_record and return True.

You will also at this point want to comment out or remove the print_short_record function in the analysis_functions list so that only records matching the check will be printed rather than all records:

analysis_functions = (
    # print_short_record,
    deleting_logs,
    instance_creds_used_outside_ec2,
)

Bonus round!

Expand the check to look for an API call that stops logging on a trail in CloudTrail (without deleting the trail).

Reference

Curious about what some of the log deletion API calls do? Here are some docs to check out:

Phase 2: Off-instance usage of instance credentials

The usage of session credentials from a running EC2 instance from outside of that instance is a potential indicator that an attacker may have obtained leaked or stolen credentials.

Implement code for the instance_creds_used_outside_ec2 function to check for API calls made using EC2 instance credentials "off-instance", or in other words, credentails that have been removed from the instance and are being used outside of it.

Examine each record to look for ones that satisfy the following 3 properties for API calls made using instance credentials:

  1. User making the call is using an assumed role
  2. The user's access key is a session key that begins with the string 'AS' instead of 'AK'
  3. The user's ARN ends with an instance identifier consisting of the string 'i-' followed by 8 or more alphanumeric characters in the username portion (e.g., i-d34db33f)

For #3, a regular expression pattern called instance_identifier_arn_pattern has been predefined for you to use. You can use it with Python's match function that returns True if the pattern matches and False otherwise:

arn_matches = instance_identifier_arn_pattern.match(arn)
if arn_matches:
    print('ARN appears to contain an instance identifier!')

When a matching record is found, print it out using print_short_record and return True.

Hint: The event you should find in this phase is an s3:GetBucketPolicy API call. Please note that if you are looking for this event in the CloudTrail console's Event History, you will not see it there because it is a read-only action and the Event History only shows create, modify, or delete actions.

Reference

Curious about what instance credentials are? See this documentation for more:

http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/iam-roles-for-amazon-ec2.html#instance-metadata-security-credentials

Phase 3: Sending metrics to CloudWatch to trigger an alarm

The CloudFormation template created a CloudWatch alarm with the following properties:

  • ComparisonOperator: GreaterThanOrEqualToThreshold
  • EvaluationPeriods: 1
  • MetricName: "AnomaliesDetected"
  • Namespace: "AWS/reInvent2017/SID341"
  • Period: 60
  • Statistic: "Sum"
  • Threshold: 1.0

This means that if you put metric data to CloudWatch using MetricName AnomaliesDetected, Namespace AWS/reInvent2017/SID341, and a Value of 1, the CloudWatch alarm will fire by going into ALARM state.

There is a pre-defined CloudWatch Boto client in the handler that you can use for this:

cloudwatch = session.client('cloudwatch')

When the CloudWatch alarm fires, if you had set up the NotificationEmailAddress parameter earlier when creating the CloudFormation stack, you will receive an email about the alarm firing. You can also browse to the CloudWatch console and click on Alarms in the left menu to view the alarm and its current state.

Extra credit: Alarming improvements

These metrics, and the accompanying alarm, are quite simple, but it is straightforward to adjust this to have, for instance, separate alarms for each analysis function, or set different triggering conditions for the alarm. You could have separate alarms for each analysis function by changing the MetricName accordingly, and updating the cloudformation.yaml template to create alarms for each metric.

For more information, please visit the following CloudWatch User Guide pages:

Cleaning up

To delete the CloudFormation stack, a Bash script, teardown.sh, has been provided. Run as follows:

./teardown.sh

  • Q: The script is not running.

  • A: It may need to be made executable. Do chmod +x teardown.sh to fix this.

  • Q: I'm using a different AWS CLI profile than the default.

  • A: The script supports a flag to specify a CLI profile that is configured in your ~/.aws/config file. Do ./teardown.sh -p PROFILE_NAME.

Manual clean-up instructions

To delete the CloudFormation stack manually, it's a two-step process, since you have to delete the S3 buckets first otherwise the CloudFormation console's delete stack operation will report a "DELETE FAILED" error since the S3 bucket still have contents.

  1. Go to the S3 console and delete both of the S3 buckets that the template created; their names begin with the following strings (note that there will be some random characters at the end of each bucket's name that CloudFormation inserts when deploying):
  • reinvent2017-sid341-activitygenbucket
  • reinvent2017-sid341-cloudtrailbucket
  1. Go to the CloudFormation console and delete the stack. Now that the S3 buckets are gone, this will work.

Walkthrough guide for Exercise 2: Automated detection

This walkthrough will give full details on how to complete each phase of the automated detection exercise, including finished code snippets that can be copied and pasted into the Lambda function.

Phase 1: Deleting logs

To solve this phase, you need to examine each record to look for ones with an eventSource of logs.amazonaws.com or cloudtrail.amazonaws.com and an eventName that starts with the string "Delete".

The completed function will look like the following:

def deleting_logs(record):
    """
    Checks for API calls that delete logs in CloudWatch Logs or CloudTrail.

    :return: True if record matches, False otherwise
    """
    event_source = record['eventSource']
    event_name = record['eventName']

    if event_source in ['logs.amazonaws.com', 'cloudtrail.amazonaws.com']: 
        if event_name.startswith('Delete'):
            print_short_record(record)
            return True

    return False

You will also at this point want to comment out or remove the print_short_record function in the analysis_functions list so that only records matching the check will be printed rather than all records:

analysis_functions = (
    # print_short_record,
    deleting_logs,
    instance_creds_used_outside_ec2,
)

Bonus round

For the bonus round, add the following check to the function:

if event_source == 'cloudtrail.amazonaws.com' and event_name == 'StopLogging':
    print_short_record(record)
    return True

Phase 2: Off-instance usage of instance credentials

To solve this phase, you must implement checks of the 3 properties that were specified:

  1. userIdentity.type is AssumedRole
  2. userIdentity.accessKey begins with string 'AS' instead of 'AK'
  3. userIdentity.arn matches the instance_identifier_arn_pattern

Please note that the check in #3 is imperfect beacuse we don't know whether that is or was a valid instance identifier for an instance in this account (or any account, for that matter), since the username could technically be set to something that looks like an instance identifier, which could lead to a false positive. However, for our purposes in this workshop it will suffice. Improving this check will be left as extra credit for the reader ;)

The completed function will look like the following:

instance_identifier_arn_pattern = re.compile(r'(.*?)/i\-[a-zA-Z0-9]{8,}$')

def instance_creds_used_outside_ec2(record):
    """
    Check for usage of EC2 instance credentials from outside the EC2 service.

    :return: True if record matches, False otherwise
    """
    identity = record['userIdentity']

    # First, check that the role type is assumed role
    role_type = identity.get('type', '')
    if role_type != 'AssumedRole':
        return False

    # Next, check that the AKID starts with 'AS'
    access_key = identity.get('accessKeyId', '')
    if not access_key.startswith('AS'):
        return False

    # Finally, check that the end of the user ARN is an instance identifier
    arn = identity.get('arn', '')
    if instance_identifier_arn_pattern.match(arn):
        print_short_record(record)
        return True

    return False

Phase 3: Sending metrics to CloudWatch to trigger an alarm

You can use the following code snippet at the end of your Lambda function handler, inside the for loop that iterates over each record:

if func(record):
    cloudwatch.put_metric_data(
        Namespace='AWS/reInvent2017/SID341',
        MetricData=[{
            'MetricName': 'AnomaliesDetected',
            'Value': 1,
            'Unit': 'Count',
        }]
    )

It uses the pre-defined CloudWatch Boto client to put metric data to CloudWatch for a specific metric that the CloudWatch alarm is monitoring.

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