Extract DPAPI-protected secrets such as credentials and browser data offline and online.
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andycungkrinx91/konoha10Updated Oct 21, 2026
FreePublished on GitHub
detecting-aws-cloudtrail-anomalies
Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
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Works withAWS
See how to installFiles included (3)
- SKILL.md2.9 KB
- references/api-reference.md2.1 KB
- scripts/agent.py7.3 KB
SKILL.md (original)
Review the instructions the agent will receive before you install.
Detecting AWS CloudTrail Anomalies
Overview
AWS CloudTrail records API calls across AWS services. This skill covers querying CloudTrail events with boto3's lookup_events API, building statistical baselines of normal API activity, detecting anomalies such as unusual event sources, geographic anomalies, high-frequency API calls, and first-time API usage patterns that indicate compromised credentials or insider threats.
When to Use
- When investigating security incidents that require detecting aws cloudtrail anomalies
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Python 3.9+ with
boto3library - AWS credentials with CloudTrail read permissions (cloudtrail:LookupEvents)
- Understanding of AWS IAM and common API patterns
- CloudTrail enabled in target AWS account (management events at minimum)
Steps
Step 1: Query CloudTrail Events
Use boto3 CloudTrail client's lookup_events to retrieve recent API activity with pagination.
Step 2: Build Activity Baseline
Aggregate events by user, source IP, event source, and event name to establish normal behavior patterns.
Step 3: Detect Anomalies
Flag unusual patterns: new event sources per user, first-time API calls, geographic IP changes, high error rates, and sensitive API usage (IAM, KMS, S3 policy changes).
Step 4: Generate Detection Report
Produce a JSON report with anomaly scores, top suspicious users, and recommended investigation actions.
Expected Output
JSON report with event statistics, baseline deviations, anomalous users/IPs, sensitive API calls, and error rate analysis.
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