analyzing-campaign-attribution-evidencelisted

# Analyzing Campaign Attribution Evidence ## Overview Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attribution indicators using the Diamond Model and ACH (Analysis of Competing Hypotheses), analyzing infrastructure overlaps, TTP consistency, malware code similarities, operational timing patterns, and language artifacts to build confidence-weighted attribution assessments. ## When to Use - When investigating security incidents that require analyzing campaign attribution evidence - 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 `attackcti`, `stix2`, `networkx` libraries - Access to threat intelligence platforms (MISP, OpenCTI) - Understanding of Diamond Model of Intrusion Analysis - Familiarity with MITRE ATT&CK threat group profiles - Knowledge of malware analysis and infrastructure tracking techniques ## Key Concepts ### Attribution Evidence Categories 1. **Infrastructure Overlap**: Shared C2 servers, domains, IP ranges, hosting providers 2. **TTP Consistency**: Matching ATT&CK techniques and sub-techniques across campaigns 3. **Malware Code Similarity**: Shared code bases, compilers, PDB paths, encryption routines