v1.0 to v1.0
36 added, 2 removed. Audit A to A.
---
name: detecting-aws-cloudtrail-anomalies
- description: 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.
+ description: 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.
domain: cybersecurity
subdomain: cloud-security
tags:
- cloud-security
- aws
- cloudtrail
- anomaly-detection
- threat-detection
- boto3
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- PR.IR-01
- ID.AM-08
- GV.SC-06
- DE.CM-01
+ mitre_attack:
+ - T1078.004
+ - T1580
+ - T1538
+ - T1098.001
+ - T1526
+ mitre_f3:
+ version: '1.1'
+ tactics:
+ - initial-access
+ - positioning
+ - defense-impairment
+ techniques:
+ - id: F1006.001
+ name: 'Account Takeover: Exposed API Key'
+ tactic: initial-access
+ source: f3
+ - id: T1586.003
+ name: 'Compromise Accounts: Cloud Accounts'
+ tactic: resource-development
+ source: attack
+ - id: F1005
+ name: Account Manipulation
+ tactic: positioning
+ source: f3
+ - id: F1005.002
+ name: 'Account Manipulation: Add Authorized User'
+ tactic: positioning
+ source: f3
+ - id: F1005.001
+ name: 'Account Manipulation: Account Linking'
+ tactic: defense-impairment
+ source: f3
---
# 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 `boto3` library
- 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.