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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
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| name | analyzing-campaign-attribution-evidence |
| 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"] |
| version | 1.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | ["ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02"] |
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.
Most attribution errors come from treating shared resources as unique fingerprints. Watch these gaps:
neutral in the ACH matrix, never consistent.attackcti group-to-technique mappings lag real activity; a low Jaccard score may reflect outdated ATT&CK group data, not a different actor.To validate the analysis works: seed the ACH matrix with a known-good campaign and confirm the true actor ranks first; verify inconsistent evidence is weighted (-2x) so a single contradiction demotes a hypothesis. Re-run rank_hypotheses() after removing the single strongest evidence item -- if the top actor does not change, attribution rests on one point and should be downgraded to MODERATE/LOW. Document at least one competing hypothesis and an explicit false-flag check for every HIGH assessment.
attackcti, stix2, networkx librariesStructured 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.
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"
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",
}
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,
}
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
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