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dcx7c5/ai-marketplace/collect

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PublishedApril 29, 2026 at 06:43 AM

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version: "1.0.0" name: analysis-campaign-collect description: 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

nist_csf:

ID.RA-01
ID.RA-05
DE.CM-01
DE.AE-02

model: sonnet maxTurns: 20 tools: [Read, Bash, Glob, Grep] capec: []

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

Infrastructure Overlap: Shared C2 servers, domains, IP ranges, hosting providers
TTP Consistency: Matching ATT&CK techniques and sub-techniques across campaigns
Malware Code Similarity: Shared code bases, compilers, PDB paths, encryption routines
Operational Patterns: Timing (working hours, time zones), targeting patterns, operational tempo
Language Artifacts: Embedded strings, variable names, error messages in specific languages
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

Step 1: Collect Attribution Evidence

python

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

python

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

python

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

python

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

References

All versions