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分析威胁情报源

分析结构化和非结构化的威胁情报源,提取高质量 IOCs 并归一化到统一格式。

分析威胁情报源 是什么?

该技能适用于安全分析师和 SOC 团队,专门用于处理来自商业和开源情报(OSINT)的海量威胁情报源。其核心价值在于自动化执行从接入、评分、标准化(STIX 2.1)、去重、富化到分发到 SIEM/EDR 等下游系统的全流程。支持多种平台,包括 ThreatConnect、MISP、OpenCTI 等 TIP,并集成了 Recorded Future、VirusTotal、Shodan 等外部服务。工作流基于 TAXII 2.1 协议和 REST API 进行数据摄取,通过加权评分矩阵评估源的时效性和准确性,并利用 OASIS 标准 STIX 2.1 模式对各类 IOC(IP、域名、哈希等)进行归一化。此外,它强制实施 TTL 策略以避免过时 IOC 产生的误报,并通过置信度分数实现不同级别的自动化响应(检测 vs 阻断)。该技能能显著提升威胁狩猎的效率和精度,减少分析师的重复劳动。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-threat-intelligence-feeds

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文档

分析威胁情报源 是做什么的?

何时使用

在以下情况下使用此技能:

  • 接收新的商业或开源威胁情报源,并评估其信噪比
  • 将异构 IOC 格式(STIX 2.1、OpenIOC、YARA、Sigma)归一化为统一架构
  • 评估情报源的新鲜度、准确性和与组织威胁态势的相关性
  • 构建自动化的富化管道,将 IOC 与 SIEM 事件关联

不要将此技能用于原始数据包捕获分析或未经建立CTI基线的实时事件响应。

前提条件

  • 访问威胁情报平台 (TIP),如 ThreatConnect、MISP 或 OpenCTI
  • 至少一个商业情报源的 API 密钥(如 Recorded Future、Mandiant Advantage 或 VirusTotal Enterprise)
  • TAXII 2.1 客户端库(taxii2-client Python 包或其等效库)
  • 对 TIP 的指标数据库具有读写权限的角色

工作流

步骤 1:枚举并排定情报源优先级

按类别列出所有可用情报源(商业、政府、ISAC、OSINT):

  • 商业:Recorded Future、Mandiant Advantage、CrowdStrike Falcon Intelligence
  • 政府:CISA AIS(自动指标共享)、FBI InfraGard、MS-ISAC
  • OSINT:AlienVault OTX、Abuse.ch、PhishTank、Emerging Threats

根据以下标准对每个情报源打分:更新频率、历史准确性、对所在行业的覆盖度、以及归因深度。使用基于 NIST SP 800-150(网络威胁信息共享指南)标准的加权评分矩阵。

步骤 2:通过 TAXII 2.1 或 API 摄取

对于支持 TAXII 的情报源:

taxii2-client discover https://feed.example.com/taxii/
taxii2-client get-collection --collection-id <id> --since 2024-01-01

对于 REST API 源(如 Recorded Future):

  • 查询 /v2/indicator/search 并设置 risk_score_min=65 以过滤低置信度 IOC
  • 应用速率限制和指数退避策略以确保 API 韧性

步骤 3:归一化为 STIX 2.1

使用 OASIS 标准架构将每个 IOC 转换为 STIX 2.1 对象:

  • IP 地址 → indicator 对象,使用 pattern: "[ipv4-addr:value = '...']"
  • 域名 → indicator 对象,使用 pattern: "[domain-name:value = '...']"
  • 文件哈希 → indicator 对象,使用 pattern: "[file:hashes.SHA-256 = '...']"

附加 relationship 对象,将指标链接到 threat-actor(威胁行为体) 或 malware(恶意软件) 对象。根据源准确度评级填写 confidence(置信度)字段(0–100)。

步骤 4:去重与富化

使用归一化后的值+类型作为复合键,对现有 TIP 数据库进行去重处理。对存活的 IOC 进行富化:

  • VirusTotal:检测率、沙箱行为报告
  • PassiveTotal (RiskIQ):WHOIS 历史、被动 DNS、SSL 证书链
  • Shodan:横幅数据、开放端口、地理位置

步骤 5:分发至消费系统

通过 TAXII 2.1 推送将富化后的指标导出至 SIEM(Splunk、Microsoft Sentinel)、防火墙(Palo Alto XSOAR playbooks)以及 EDR 平台。根据指标类型设置 TTL:IP 地址 30 天、域名 90 天、文件哈希 1 年。

关键概念

术语定义
STIX 2.1结构化威胁信息表达 — OASIS 标准 JSON 架构,用于 CTI 对象,包括指标、威胁行为体、攻击活动和关系
TAXII 2.1可信自动化威胁情报信息交换 — 基于 HTTPS 的协议,用于在服务器和客户端之间共享 STIX 内容
IOC危害指标 — 可观察到的工件(IP、域名、哈希、URL),表明系统可能已被入侵
TLP交通灯协议 — 颜色编码分类(红/琥珀/绿/白),定义 CTI 的共享限制
置信度分数数值(STIX 中为 0–100),反映情报源对某个指标恶意归因的确定性
情报源准确度情报源的历史准确率,通过生产环境检测中的真阳性率衡量

工具与系统

  • ThreatConnect TC Exchange:聚合 100+ 商业和 OSINT 情报源;提供用于 IOC 富化的自动化剧本
  • MISP(恶意软件信息共享平台):支持 STIX/TAXII 的开源 TIP;被 ISAC 和政府 CERT 广泛使用
  • OpenCTI:原生集成 MITRE ATT&CK 和基于图的关系可视化的开源平台
  • Recorded Future:具有 AI 驱动风险评分和实时暗网监控的商业情报源
  • taxii2-client:用于 TAXII 2.0/2.1 客户端操作的 Python 库(pip install taxii2-client)
  • PyMISP:用于 MISP 情报源管理和 IOC 提交的 Python API

常见陷阱

  • IOC 过时:IP 地址和域名频繁轮换;应用一年前的 IOC 会产生误报。强制执行 TTL 策略。
  • 缺乏上下文:在不了解相关攻击活动或对手的情况下阻断 IOC,可能会干扰合法业务流量(例如与恶意行为者共享的 CDN IP)。
  • 未去重的情报源重叠:从五个源摄取同一个 IOC 而不去重,会放大指标数量并增加 SIEM 规则复杂性。
  • 违反 TLP:在授权边界之外分发 RED 分类情报会违反共享协议和信任关系。
  • 基于低置信度指标过度阻断:置信度低于 50 的指标应触发仅检测规则,而非阻断,以避免运营中断。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

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acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

analyzing-apt-group-with-mitre-navigator

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

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.

analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cloud-storage-access-patterns

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

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