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使用NetFlow分析网络流量数据

解析 NetFlow v9 和 IPFIX 记录,检测容量异常、端口扫描等安全威胁。

使用NetFlow分析网络流量数据 是什么?

该技能旨在帮助安全分析师或网络运维人员利用 NetFlow v9 和 IPFIX 协议解析网络流量数据,识别安全事件和异常行为。它提供了从数据采集、解析到分析的一整套流程,支持检测端口扫描、数据外泄、C2 信标通信以及流量容量异常。技能适用于 Python 3.8+ 环境,依赖 `netflow` 库,并提供了示例代码和命令行脚本。通过该技能,用户可以快速构建检测规则或威胁狩猎查询,验证安全监控覆盖范围,适合 SOC 分析师和红蓝队使用。支持平台包括 Claude Code、Cursor 等 AI 代理工具。

兼容平台~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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打开一个已预加载此 Agent Skill 的新对话。

文档

使用 NetFlow 分析网络流量数据

适用场景

  • 调查需要使用 NetFlow 分析网络流量数据的安全事件时
  • 为此领域构建检测规则或威胁狩猎查询时
  • SOC 分析师需要此类分析的结构化流程时
  • 验证相关攻击技术的安全监控覆盖范围时

前置条件

  • 熟悉网络安全概念和工具
  • 拥有测试或实验环境以安全执行操作
  • 已安装 Python 3.8+ 及其所需依赖
  • 拥有进行测试活动的适当授权

操作指南

  1. 安装依赖:pip install netflow
  2. 从路由器收集 NetFlow/IPFIX 数据,或使用内置收集器:python -m netflow.collector -p 9995
  3. 使用 netflow.parse_packet() 解析捕获的流量数据。
  4. 分析流量中的以下内容:
    • 端口扫描:同一源 IP 向多个目标 IP 同一端口的连接
    • 数据外泄:发往异常目标、字节数较高的出站流量
    • C2 信标通信:周期性的、间隔一致的连接
    • 容量异常:超过基线阈值的流量突增
  5. 生成按优先级排列的发现报告。
python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json

示例

解析 NetFlow v9 数据包

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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