mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
61 lines
2.2 KiB
Markdown
61 lines
2.2 KiB
Markdown
---
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name: detecting-aws-cloudtrail-anomalies
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description: Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis
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to identify credential compromise, privilege escalation, and unauthorized resource access.
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domain: cybersecurity
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subdomain: cloud-security
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tags:
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- cloud-security
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- aws
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- cloudtrail
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- anomaly-detection
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- threat-detection
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- boto3
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- PR.IR-01
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- ID.AM-08
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- GV.SC-06
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- DE.CM-01
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---
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# Detecting AWS CloudTrail Anomalies
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## Overview
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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.
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## When to Use
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- When investigating security incidents that require detecting aws cloudtrail anomalies
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Python 3.9+ with `boto3` library
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- AWS credentials with CloudTrail read permissions (cloudtrail:LookupEvents)
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- Understanding of AWS IAM and common API patterns
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- CloudTrail enabled in target AWS account (management events at minimum)
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## Steps
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### Step 1: Query CloudTrail Events
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Use boto3 CloudTrail client's lookup_events to retrieve recent API activity with pagination.
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### Step 2: Build Activity Baseline
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Aggregate events by user, source IP, event source, and event name to establish normal behavior patterns.
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### Step 3: Detect Anomalies
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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).
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### Step 4: Generate Detection Report
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Produce a JSON report with anomaly scores, top suspicious users, and recommended investigation actions.
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## Expected Output
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JSON report with event statistics, baseline deviations, anomalous users/IPs, sensitive API calls, and error rate analysis.
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