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使用Scapy分析網路封包

使用Scapy來製作、發送、嗅探及剖析TCP/UDP/ICMP/DNS封包，分析pcap檔案，執行SYN掃描，並偵測如碎片化或畸形封包等異常流量。適用於執行授權的網路偵察、協定層級的鑑識分析，或在安全測試期間建構流量異常偵測。

使用Scapy分析網路封包 是什麼？

此技能詳述如何使用Scapy（一個功能強大的Python封包操作函式庫）來執行各種網路安全相關任務。它能讓開發者或安全分析師透過直觀的Python程式碼，在協定層級上製作、傳送、嗅探及剖析網路封包。其主要用途涵蓋分析pcap/pcapng檔案以進行離線鑑識、實作SYN掃描進行授權網路偵察、深入分析DNS查詢以找出潛在的資料外洩跡象，以及偵測異常流量模式（如SYN洪水或碎片化攻擊）。此技能設計為跨平台相容（支援Linux、macOS、Windows），但執行原始套接字操作（如傳送封包）時需要管理員或根權限。透過遵循一系列結構化步驟（從讀取封包檔案到匯出結構化JSON報告），使用者可以自動化重複性分析工作流程，並將結果無縫整合到現有的安全監控或威脅獵捕系統中。

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說明文件

使用Scapy分析網路封包 是做什麼的？

概述

Scapy 是一個 Python 封包操作函式庫，能夠在精細的協定層級上製作、傳送、嗅探及剖析網路封包。本技能涵蓋將 Scapy 應用於安全相關任務，包括 TCP/UDP/ICMP 封包製作、pcap 檔案分析、協定欄位擷取、SYN 掃描實作、DNS 查詢分析，以及偵測異常流量模式，例如異常碎片化的封包或畸形標頭。

使用時機

  • 當調查需要使用 Scapy 分析網路封包的安全事件時
  • 當為此領域建立偵測規則或威脅獵捕查詢時
  • 當 SOC 分析師需要此分析類型的結構化程序時
  • 當驗證相關攻擊技術的安全監控覆蓋範圍時

先決條件

  • Python 3.8+ 且已安裝 scapy 函式庫（pip install scapy）
  • 用於原始套接字操作（嗅探、傳送）的 root/系統管理員權限
  • 用於封包擷取的 Npcap（Windows）或 libpcap（Linux）
  • 獲得在目標網路上執行封包操作的授權

步驟

  1. 使用 rdpcap() 讀取並解析 pcap/pcapng 檔案以進行離線分析
  2. 擷取協定層（IP、TCP、UDP、DNS、HTTP）及其欄位值
  3. 計算流量統計數據：熱門通訊端、協定分佈、連接埠頻率
  4. 透過分析 TCP 旗標比率來偵測 SYN 洪水模式
  5. 透過查詢長度與熵分析來識別 DNS 外洩指標
  6. 製作自訂探測封包以進行授權的網路測試
  7. 將發現結果匯出為結構化 JSON 報告

預期輸出

包含封包統計數據、協定分佈、頂層來源/目的地 IP、偵測到的異常（SYN 洪水、DNS 通道化指標、碎片化攻擊）以及每個流量的摘要的 JSON 報告。

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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