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.
适合你，如果你需要识别 AWS 账户中的可疑活动或潜在安全威胁。
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~/.claude/skills/(项目级 .claude/skills/)~/.codex/skills/npx oh-my-skill add adriannoes/awesome-vibe-coding/detecting-aws-cloudtrail-anomaliescurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- adriannoes/awesome-vibe-coding/detecting-aws-cloudtrail-anomaliesnpx oh-my-skill verify adriannoes/awesome-vibe-coding/detecting-aws-cloudtrail-anomalies怎么用
商店整理自技能原文 · 版本 e4ed3a9 · 表述以原文为准装上后，Claude 能分析 AWS CloudTrail 日志，检测异常 API 调用模式，如凭证泄露、权限提升和未授权资源访问，并生成 JSON 报告。
当需要调查 AWS 安全事件、构建检测规则或进行威胁狩猎时触发。
技能原文 SKILL.md
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.