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analyse-du-paysage-des-menaces-avec-misp

Interrogez une instance MISP (plateforme de partage d'informations sur les malwares) via PyMISP

Qu'est-ce que analyse-du-paysage-des-menaces-avec-misp ?

Cette compétence permet aux analystes SOC et aux chasseurs de menaces d'analyser le paysage des menaces en interrogeant une instance MISP via PyMISP. Elle automatise la collecte de statistiques sur les événements, la répartition des types d'attributs (IP, domaine, hash, URL), l'identification des techniques MITRE ATT&CK, le suivi des acteurs malveillants via les clusters de galaxies, et la génération de tendances temporelles sur les soumissions d'IOC. L'utilisateur fournit l'URL de l'instance MISP et une clé API, et le script produit un rapport JSON structuré. Cette compétence est particulièrement utile pour valider la couverture de détection, élaborer des règles de détection, ou mener des enquêtes structurées. Elle nécessite Python 3.8+, la bibliothèque PyMISP, et une autorisation appropriée sur l'instance MISP ciblée.

Compatible avec~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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Documentation

Analyse du paysage des menaces avec MISP

Quand l'utiliser

  • Lors d'enquêtes sur des incidents de sécurité nécessitant une analyse du paysage des menaces avec MISP
  • Lors de la création de règles de détection ou de requêtes de chasse aux menaces pour ce domaine
  • Lorsque les analystes SOC ont besoin de procédures structurées pour ce type d'analyse
  • Lors de la validation de la couverture de surveillance de sécurité pour les techniques d'attaque associées

Prérequis

  • Familiarité avec les concepts et outils de renseignement sur les menaces
  • Accès à un environnement de test ou laboratoire pour une exécution sécurisée
  • Python 3.8+ avec les dépendances requises installées
  • Autorisation appropriée pour toute activité de test

Instructions

  1. Installer les dépendances : pip install pymisp
  2. Configurer l'URL de l'instance MISP et la clé API.
  3. Exécuter l'agent pour générer une analyse du paysage des menaces :
    • Extraire les statistiques des événements par niveau de menace et intervalle de dates
    • Analyser les répartitions des types d'attributs (IP, domaine, hash, URL)
    • Identifier les principales techniques MITRE ATT&CK à partir des étiquettes des événements
    • Suivre l'activité des acteurs malveillants via les clusters de galaxies
    • Générer une analyse des tendances temporelles des soumissions d'IOC
python scripts/agent.py --misp-url https://misp.local --api-key VOTRE_CLE --days 90 --output rapport_paysage.json

Exemples

Résumé du paysage des menaces

Période : 90 derniers jours
Événements analysés : 1 247
Niveau de menace principal : Élevé (43%)
Type d'attribut principal : ip-dst (31%), domaine (22%), sha256 (18%)
Technique MITRE principale : T1566 Hameçonnage (89 événements)
Acteur malveillant principal : APT28 (34 événements)

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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