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analizando-indicadores-de-red-de-ransomware

Identifica indicadores de red relacionados con ransomware, incluyendo patrones de beaconing C2, conexiones a nodos de salida TOR, flujos de exfiltración de datos e intercambio de claves de cifrado, mediante el análisis de datos de Zeek conn.log y NetFlow. Útil durante la búsqueda de amenazas de actividad de red activa de ransomware o la investigación de posible exfiltración previa al cifrado durante la respuesta a incidentes.

¿Qué es analizando-indicadores-de-red-de-ransomware?

Esta habilidad está diseñada para analizar registros de red de Zeek conn.log y datos NetFlow con el fin de detectar indicadores clave de actividad de ransomware antes y durante su ejecución. Los adversarios establecen canales de comando y control (C2), exfiltran datos y descargan claves de cifrado; la habilidad identifica patrones de beaconing (llamadas periódicas), conexiones a nodos de salida TOR conocidos, transferencias de datos salientes inusualmente grandes y actividad DNS sospechosa asociada a familias de ransomware. Es especialmente útil para analistas de seguridad durante la caza de amenazas o la respuesta a incidentes, ya que proporciona un procedimiento estructurado para detectar estas actividades maliciosas. La habilidad es compatible con entornos que generan registros Zeek o NetFlow y requiere Python 3.8+. Su salida incluye un informe JSON con detecciones de beaconing, alertas de conexiones TOR, análisis de flujos de exfiltración y una puntuación de riesgo compuesta mapeada a técnicas MITRE ATT&CK como T1071, T1573 y T1041, facilitando la correlación de amenazas y la generación de informes de incidentes.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-ransomware-network-indicators
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Documentación

Análisis de Indicadores de Red de Ransomware

Resumen

Antes y durante la ejecución del ransomware, los adversarios establecen canales C2, exfiltran datos y descargan claves de cifrado. Esta habilidad analiza datos de Zeek conn.log y NetFlow para detectar patrones de beaconing (llamadas periódicas de intervalo regular), conexiones a nodos de salida TOR conocidos, transferencias de datos salientes de gran tamaño y actividad DNS sospechosa asociada a familias de ransomware.

Cuándo Usar

  • Al investigar incidentes de seguridad que requieran analizar indicadores de red de ransomware
  • Al construir reglas de detección o consultas de caza de amenazas para este dominio
  • Cuando los analistas del SOC necesiten procedimientos estructurados para este tipo de análisis
  • Al validar la cobertura de monitoreo de seguridad para técnicas de ataque relacionadas

Prerrequisitos

  • Archivos Zeek conn.log o exportaciones NetFlow en CSV/JSON
  • Python 3.8+ con biblioteca estándar
  • Lista de nodos de salida TOR (obtenida de Tor Project o fuentes de inteligencia de amenazas)
  • Opcional: Lista de IOC de C2 de ransomware conocido

Pasos

  1. Analizar Registros de Conexión — Ingerir Zeek conn.log (TSV) o registros NetFlow en un formato estructurado
  2. Detectar Patrones de Beaconing — Calcular estadísticas de intervalos de conexión (media, desviación estándar, coeficiente de variación) para identificar llamadas periódicas
  3. Verificar Conexiones a Nodos de Salida TOR — Comparar las IPs de destino con la lista actual de nodos de salida TOR
  4. Identificar Exfiltración de Datos — Marcar conexiones con relaciones de bytes salientes inusualmente altas hacia IPs externas
  5. Analizar Patrones DNS — Detectar consultas de dominio similares a DGA y subdominios de alta entropía
  6. Puntuar y Correlacionar — Aplicar una puntuación de riesgo compuesta a través de todos los tipos de indicadores
  7. Generar Informe — Producir un informe estructurado con línea de tiempo y mapeo MITRE ATT&CK

Salida Esperada

  • Informe JSON con detecciones de beaconing y estadísticas de intervalos
  • Alertas de conexiones a nodos de salida TOR
  • Análisis de flujos de exfiltración de datos
  • Puntuación de riesgo compuesta de ransomware con mapeo MITRE (T1071, T1573, T1041)

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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