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análisis-de-datos-de-flujo-de-red-con-netflow

Analiza registros NetFlow v9 e IPFIX para detectar anomalías volumétricas, escaneos de puertos, exfiltración de datos y beaconing de C2.

¿Qué es análisis-de-datos-de-flujo-de-red-con-netflow?

Esta habilidad permite a los agentes de IA (como Claude Code, Cursor o Codex) analizar datos de flujo de red utilizando los protocolos NetFlow v9 e IPFIX. Está diseñada para investigadores de seguridad, analistas SOC y desarrolladores de reglas de detección que necesitan identificar patrones maliciosos en el tráfico de red, como escaneos de puertos, exfiltración de datos, beaconing de C2 y anomalías volumétricas. La habilidad se basa en la biblioteca Python `netflow` para la captura y análisis de flujos, y proporciona un script de agente (agent.py) que procesa archivos JSON de flujos capturados y genera informes priorizados. Es especialmente útil durante investigaciones de incidentes o para validar la cobertura de monitoreo de seguridad. La ejecución requiere Python 3.8+, dependencias instaladas y autorización adecuada para pruebas en entornos controlados. El flujo de trabajo típico incluye instalar dependencias, recolectar datos NetFlow/IPFIX (usando un recolector integrado), analizar los flujos para detectar los patrones mencionados, y generar un informe de hallazgos priorizados.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-network-flow-data-with-netflow

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Documentación

Análisis de Datos de Flujo de Red con Netflow

Cuándo Usarlo

  • Cuando se investigan incidentes de seguridad que requieren analizar datos de flujo de red con netflow
  • Cuando se construyen reglas de detección o consultas de búsqueda de amenazas para este dominio
  • Cuando los analistas SOC necesitan procedimientos estructurados para este tipo de análisis
  • Cuando se valida la cobertura de monitoreo de seguridad para técnicas de ataque relacionadas

Prerrequisitos

  • Familiaridad con conceptos y herramientas de seguridad de red
  • Acceso a un entorno de prueba o laboratorio para ejecución segura
  • Python 3.8+ con las dependencias requeridas instaladas
  • Autorización adecuada para cualquier actividad de prueba

Instrucciones

  1. Instalar dependencias: pip install netflow
  2. Recolectar datos NetFlow/IPFIX de routers o usar el recolector integrado: python -m netflow.collector -p 9995
  3. Analizar los datos de flujo capturados usando netflow.parse_packet().
  4. Analizar flujos para detectar:
    • Escaneo de puertos: un solo origen hacia muchos destinos en el mismo puerto
    • Exfiltración de datos: flujos salientes con alto conteo de bytes hacia destinos inusuales
    • Beaconing de C2: conexiones periódicas con intervalos consistentes
    • Anomalías volumétricas: picos de tráfico más allá de los umbrales de referencia
  5. Generar un informe de hallazgos priorizados.
python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json

Ejemplos

Analizar un Paquete NetFlow v9

import netflow
data, _ = netflow.parse_packet(raw_bytes, templates={})
for flow in data.flows:
    print(flow.IPV4_SRC_ADDR, flow.IPV4_DST_ADDR, flow.IN_BYTES)

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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