analyzing-campaign-attribution-evidence

name	analyzing-campaign-attribution-evidence
description	Use when 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 attr
domain	cybersecurity
subdomain	threat-intelligence
tags	["threat-intelligence","cti","ioc","mitre-attack","stix","attribution","campaign-analysis"]
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02"]

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

Trigger phrases:

• "analyzing campaign attribution evidence"

• "Campaign attribution analysis involves systematically evaluating evidence to det"

• 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

This section covers key concepts for analyzing campaign attribution evidence.

• Ensure all prerequisites are met before proceeding
• Follow the documented workflow steps in sequence
• Record results and any anomalies encountered during this phase

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
4. Operational Patterns: Timing (working hours, time zones), targeting patterns, operational tempo
5. Language Artifacts: Embedded strings, variable names, error messages in specific languages
6. Victimology: Target sector, geography, and organizational profile consistency

Confidence Levels