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使用 MISP 分析威脅情勢

透過 PyMISP 查詢 MISP（惡意軟體資訊分享平台）執行個體，以進行威脅情勢分析。

使用 MISP 分析威脅情勢 是什麼？

這項技能賦予 AI 代理程式（如 Claude Code、Cursor 或 Codex）直接查詢 MISP 執行個體的能力，使用 PyMISP 函式庫。使用者可以分析安全事件、建置偵測規則、執行威脅狩獵查詢，並以結構化方式驗證安全監控覆蓋率。此技能支援自動化事件統計（依威脅層級與日期）、屬性類型分布（IP、網域、雜湊值、URL）、透過事件標籤識別 MITRE ATT&CK 頂尖技術、使用銀河星團追蹤威脅行為者活動，以及生成 IOC 提交的時序趨勢分析。運行前需安裝 Python 3.8 以上版本與 PyMISP、設定 MISP 網址與 API 金鑰，並擁有適當授權。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-threat-landscape-with-misp
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說明文件

使用 MISP 分析威脅情勢 是做什麼的？

使用時機

  • 調查需要透過 MISP 分析威脅情勢的安全事件時
  • 為此領域建置偵測規則或威脅狩獵查詢時
  • SOC 分析師需要此分析類型的結構化程序時
  • 驗證相關攻擊手法之安全監控覆蓋率時

先決條件

  • 熟悉威脅情資概念與工具
  • 可存取測試或實驗環境以安全執行
  • Python 3.8 以上版本，並安裝所需相依套件
  • 擁有任何測試活動的適當授權

操作說明

  1. 安裝相依套件：pip install pymisp
  2. 設定 MISP 網址與 API 金鑰。
  3. 執行代理程式以產生威脅情勢分析：
    • 依威脅層級與日期範圍擷取事件統計
    • 分析屬性類型分布（IP、網域、雜湊值、URL）
    • 從事件標籤中識別頂尖 MITRE ATT&CK 手法
    • 透過銀河星團追蹤威脅行為者活動
    • 生成 IOC 提交的時序趨勢分析
python scripts/agent.py --misp-url https://misp.local --api-key YOUR_KEY --days 90 --output landscape_report.json

範例

威脅情勢摘要

期間：最近 90 天
分析事件數：1,247
最高威脅層級：高（43%）
最高屬性類型：ip-dst（31%）、domain（22%）、sha256（18%）
頂尖 MITRE 手法：T1566 釣魚攻擊（89 個事件）
頂尖威脅行為者：APT28（34 個事件）

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

>-

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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