Analyzing Campaign Attribution Evidence

mukul975/Anthropic-Cybersecurity-Skills/skills/analyzing-campaign-attribution-evidence

作者 mukul97554a798831d2266a3ca61ce68a7acb80b81160d57Apache-2.034K 个星标收录于 2026年10月9日更新于 2026年10月9日仓库5周前更新

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

包含脚本SecurityResearch & Analysis
AI 生成的概览

使用钻石模型与竞争性假设分析评估网络战役证据，将攻击归因于特定威胁行为体。

功能
该技能指导对网络战役进行结构化归因分析，从六类证据入手：基础设施重叠、TTP 一致性、恶意软件代码相似性、作战模式、语言痕迹和受害者特征。它将证据对多个竞争性威胁行为体假设评为一致、不一致或中立，比较基础设施与 ATT&CK 技术集合，并将假设排序为高、中、低置信度。最终产出结构化归因评估报告，包含排序、主要归因结论和证据摘要。
适用场景
当事件调查需要为某次网络行动给出可辩护的归因置信度时使用。适合 SOC 分析师和威胁情报团队权衡相互竞争的行为体假设并记录假旗考量。
运行要求
Python 3.9+ 及 attackcti、stix2、networkx 库；可访问 MISP 或 OpenCTI 等威胁情报平台；熟悉钻石模型、MITRE ATT&CK 组织画像以及恶意软件与基础设施追踪。该技能附带可执行脚本。

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
  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

Step 1: Collect Attribution Evidence

python
from stix2 import MemoryStore, Filterfrom 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

来源与署名

来源：mukul975/Anthropic-Cybersecurity-Skills位于skills/analyzing-campaign-attribution-evidence提交54a7988

许可证： Apache-2.0

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

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