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mukul975
886658219f
- Add mitre_f3 frontmatter block to 94 fraud-relevant skills (phishing, account takeover, banking malware, BEC, identity/KYC, payment/card fraud, money-mule/cash-out, ransomware extortion, DFIR, threat intel) - Map each skill to F3 v1.1 tactics + precise technique IDs, including the two F3-specific tactics ATT&CK lacks: Positioning (FA0001) and Monetization (FA0002) - All 123 F3 v1.1 technique IDs validated against the upstream STIX bundle (github.com/center-for-threat-informed-defense/fight-fraud-framework): 0 invalid IDs, 0 invalid tactics, 0 name mismatches, no placeholder IDs - mitre_f3 kept as a separate block from mitre_attack (F3 redefines several ATT&CK tactics for the fraud context) - Add docs/mitre-f3-mapping.md schema reference - Update README: F3 as the 6th framework, dedicated F3 section + badge
95 lines
2.9 KiB
Markdown
95 lines
2.9 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,
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statistical baselining, and behavioral analysis to identify credential compromise,
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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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mitre_attack:
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- T1078.004
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- T1580
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- T1538
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- T1098.001
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- T1526
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mitre_f3:
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version: '1.1'
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tactics:
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- initial-access
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- positioning
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- defense-impairment
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techniques:
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- id: F1006.001
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name: 'Account Takeover: Exposed API Key'
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tactic: initial-access
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source: f3
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- id: T1586.003
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name: 'Compromise Accounts: Cloud Accounts'
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tactic: resource-development
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source: attack
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- id: F1005
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name: Account Manipulation
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tactic: positioning
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source: f3
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- id: F1005.002
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name: 'Account Manipulation: Add Authorized User'
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tactic: positioning
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source: f3
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- id: F1005.001
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name: 'Account Manipulation: Account Linking'
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tactic: defense-impairment
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source: f3
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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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