Extract DPAPI-protected secrets such as credentials and browser data offline and online.
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andycungkrinx91/konoha92026年10月19日 更新
免费在 GitHub 公开
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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集成工具AWS
查看安装方法包含的文件（3）
- SKILL.md2.9 KB
- references/api-reference.md2.1 KB
- scripts/agent.py7.3 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
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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