网络安全 Analyzing Campaign Attribution Evidence

Model: minimax-m3 | ¥0.20/call

网络安全Claude Opus 4.7安全审计AnalyzingCampaign

Analyzing Campaign Attribution Evidence：网络安全 skill: analyzing-campaign-attribution-evidenc，适用于安全分析、取证与威胁排查场景。

Calls: 1

Skill Documentation

网络安全 Analyzing Campaign Attribution Evidence

摘要

Analyzing Campaign Attribution Evidence：网络安全 skill: analyzing-campaign-attribution-evidenc，适用于安全分析、取证与威胁排查场景。

> 来源: mukul975/Anthropic-Cybersecurity-Skills (18k stars) — 网络安全专业技能集

> 原文件: skills/analyzing-campaign-attribution-evidence/SKILL.md

> 模型推荐: claude-opus-4-7 (安全分析深度推理)

这个 skill 是干嘛的

mukul975 整理的 100+ 个网络安全专业 skill,覆盖渗透测试 / 取证 / 威胁情报 / 合规审计 / 云安全 / 移动安全等领域。每个 skill 对应一个具体的安全分析任务。

michael 强调"skill 要有相应的指导功能,指导用户使用",所以加了下面两节让 Agent 和用户对接。

---

🤖 Agent 使用说明

1. 用户提到"分析 X 日志 / 取证 / 检测威胁 / 渗透测试 / 安全审计"时,触发对应 skill

2. skill 会按操作步骤一步步执行(取证镜像 / 解析日志 / 跑威胁情报 / etc.)

3. 涉及破坏性操作(rm/drop/format)前必须 ask user 确认

4. 完工后让 Agent 跑自检 step

5. 如果用户要求做"防御性分析" — 区分恶意代码审计 vs 渗透测试

👤 用户需要做什么?

1. 告诉 Agent 你要做什么(分析日志 / 取证 / 安全审计 / 渗透测试)

2. 如果需要提供文件/镜像/日志/哈希,按 Agent 提示提供

3. 涉及破坏性操作时明确告诉 Agent"继续"或"取消"

4. 全程 Agent 自动化,你只需提供数据 + 回答决策点

---

原 skill 内容(mukul975/Anthropic-Cybersecurity-Skills/skills/analyzing-campaign-attribution-evidence/SKILL.md,截断到 12k chars)

---

name: analyzing-campaign-attribution-evidence

description: 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.

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

mitre_attack:

T1587.001
T1583.001
T1588.002
T1071.001

---

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

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

References

[Diamond Model of Intrusion Analysis](https://www.activeresponse.org/wp-content/uploads/2013/07/diamond.pdf)
[MITRE ATT&CK Groups](https://attack.mitre.org/groups/)
[Analysis of Competing Hypotheses](https://www.cia.gov/static/9a5f1162fd0932c29e985f0159f56c07/Tradecraft-Primer-apr09.pdf)
[Threat Attribution Framework](https://www.mandiant.com/resources/reports)

常见问题（FAQ）

使用「Analyzing Camp」这个 skill 能解决什么问题?

本 skill 专注于Analyzing Camp，网络安全 skill: analyzing-campaign-attribution-evidence。它将相关流程标准化，帮助用户更快拿到可靠结果，减少重复手工操作。

什么情况下适合使用「Analyzing Camp」?

当你需要在Analyzing Campaign Attribution Evidence相关工作中获得稳定、可复用的产出时最适合——无论是单次任务还是纳入日常工作流，都能直接调用。

使用「Analyzing Camp」前需要准备什么?

需要明确授权范围内的目标系统或样本文件，并准备隔离的分析环境（虚拟机/沙箱）。

FAQ