Analyzing Campaign Attribution Evidence

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

• High Confidence: Multiple independent evidence categories converge on same actor
• Moderate Confidence: Several evidence categories match, some ambiguity remains
• Low Confidence: Limited evidence, possible false flags or shared tooling

Analysis of Competing Hypotheses (ACH)

Structured analytical method that evaluates evidence against multiple competing hypotheses. Each piece of evidence is scored as consistent, inconsistent, or neutral with respect to each hypothesis. The hypothesis with the least inconsistent evidence is favored.

Workflow

1. Prepare the environment — ensure write-blocker is connected and test workstation is ready
2. Document the source — record device serial, model, and pre-acquisition hash
3. Acquire the image — use the appropriate tool with hash verification enabled
4. Verify integrity — compare source and image hashes; document any discrepancies
5. Analyze and report — perform the analysis and document findings with chain of custody

Step 1: Collect Attribution Evidence

from stix2 import MemoryStore, Filter
from collections import defaultdict

class AttributionAnalyzer:
    def __init__(self):
        self.evidence = []
        self.hypotheses = {}

    def add_evidence(self, category, description, value, confidence):
        self.evidence.append({
            "category": category,
            "description": description,
            "value": value,
            "confidence": confidence,
            "timestamp": None,
        })

    def add_hypothesis(self, actor_name, actor_id=""):
        self.hypotheses[actor_name] = {
            "actor_id": actor_id,
            "consistent_evidence": [],
            "inconsistent_evidence": [],
            "neutral_evidence": [],
            "score": 0,
        }

    def evaluate_evidence(self, evidence_idx, actor_name, assessment):
        """Assess evidence against a hypothesis: consistent/inconsistent/neutral."""
        if assessment == "consistent":
            self.hypotheses[actor_name]["consistent_evidence"].append(evidence_idx)
            self.hypotheses[actor_name]["score"] += self.evidence[evidence_idx]["confidence"]
        elif assessment == "inconsistent":
            self.hypotheses[actor_name]["inconsistent_evidence"].append(evidence_idx)
            self.hypotheses[actor_name]["score"] -= self.evidence[evidence_idx]["confidence"] * 2
        else:
            self.hypotheses[actor_name]["neutral_evidence"].append(evidence_idx)

    def rank_hypotheses(self):
        """Rank hypotheses by attribution score."""
        ranked = sorted(
            self.hypotheses.items(),
            key=lambda x: x[1]["score"],
            reverse=True,
        )
        return [
            {
                "actor": name,
                "score": data["score"],
                "consistent": len(data["consistent_evidence"]),
                "inconsistent": len(data["inconsistent_evidence"]),
                "confidence": self._score_to_confidence(data["score"]),
            }
            for name, data in ranked
        ]

    def _score_to_confidence(self, score):
        if score >= 80:
            return "HIGH"
        elif score >= 40:
            return "MODERATE"
        else:
            return "LOW"

Step 2: Infrastructure Overlap Analysis

def analyze_infrastructure_overlap(campaign_a_infra, campaign_b_infra):
    """Compare infrastructure between two campaigns for attribution."""
    overlap = {
        "shared_ips": set(campaign_a_infra.get("ips", [])).intersection(
            campaign_b_infra.get("ips", [])
        ),
        "shared_domains": set(campaign_a_infra.get("domains", [])).intersection(
            campaign_b_infra.get("domains", [])
        ),
        "shared_asns": set(campaign_a_infra.get("asns", [])).intersection(
            campaign_b_infra.get("asns", [])
        ),
        "shared_registrars": set(campaign_a_infra.get("registrars", [])).intersection(
            campaign_b_infra.get("registrars", [])
        ),
    }

    overlap_score = 0
    if overlap["shared_ips"]:
        overlap_score += 30
    if overlap["shared_domains"]:
        overlap_score += 25
    if overlap["shared_asns"]:
        overlap_score += 15
    if overlap["shared_registrars"]:
        overlap_score += 10

    return {
        "overlap": {k: list(v) for k, v in overlap.items()},
        "overlap_score": overlap_score,
        "assessment": "STRONG" if overlap_score >= 40 else "MODERATE" if overlap_score >= 20 else "WEAK",
    }

Step 3: TTP Comparison Across Campaigns

from attackcti import attack_client

def compare_campaign_ttps(campaign_techniques, known_actor_techniques):
    """Compare campaign TTPs against known threat actor profiles."""
    campaign_set = set(campaign_techniques)
    actor_set = set(known_actor_techniques)

    common = campaign_set.intersection(actor_set)
    unique_campaign = campaign_set - actor_set
    unique_actor = actor_set - campaign_set

    jaccard = len(common) / len(campaign_set.union(actor_set)) if campaign_set.union(actor_set) else 0

    return {
        "common_techniques": sorted(common),
        "common_count": len(common),
        "unique_to_campaign": sorted(unique_campaign),
        "unique_to_actor": sorted(unique_actor),
        "jaccard_similarity": round(jaccard, 3),
        "overlap_percentage": round(len(common) / len(campaign_set) * 100, 1) if campaign_set else 0,
    }

Step 4: Generate Attribution Report

def generate_attribution_report(analyzer):
    """Generate structured attribution assessment report."""
    rankings = analyzer.rank_hypotheses()

    report = {
        "assessment_date": "2026-02-23",
        "total_evidence_items": len(analyzer.evidence),
        "hypotheses_evaluated": len(analyzer.hypotheses),
        "rankings": rankings,
        "primary_attribution": rankings[0] if rankings else None,
        "evidence_summary": [
            {
                "index": i,
                "category": e["category"],
                "description": e["description"],
                "confidence": e["confidence"],
            }
            for i, e in enumerate(analyzer.evidence)
        ],
    }

    return report

Validation Criteria

• Evidence collection covers all six attribution categories
• ACH matrix properly evaluates evidence against competing hypotheses
• Infrastructure overlap analysis identifies shared indicators
• TTP comparison uses ATT&CK technique IDs for precision
• Attribution confidence levels are properly justified
• Report includes alternative hypotheses and false flag considerations

When NOT to Use

• You need to perform the attack, not analyze it (use performing-* skills)
• Task is about detection, not analysis (use detecting-* skills)
• You need to implement controls (use implementing-* skills)
• Task is about threat hunting, not post-incident analysis (use hunting-* skills)
• You don't have access to the artifacts/logs to analyze
• Task requires real-time monitoring (use SOC tools)

Red Flags

• Performing actions without explicit written authorization from the asset owner
• Testing against production systems without a defined scope and rules of engagement
• Failing to use write-blockers when acquiring forensic evidence
• Not verifying hash integrity before and after imaging
• Modifying original evidence during analysis

Verification

• All steps executed successfully against a test environment before production use
• Output documented with screenshots or logs demonstrating expected behavior
• Hash values computed and verified match between source and image
• Chain of custody log complete with timestamps and examiner names
• Analysis tools and versions documented for reproducibility

References

• Diamond Model of Intrusion Analysis
• MITRE ATT&CK Groups
• Analysis of Competing Hypotheses
• Threat Attribution Framework

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization Table