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使用 Apktool 分析 Android 惡意軟體

使用 apktool 進行資源反編譯、jadx 進行 Java 原始碼恢復，以及 androguard 進行清單檢查，偵測危險權限組合、混淆程式碼、動態程式碼載入與基於反射的 API 呼叫，藉此對 Android APK 惡意軟體執行靜態分析。適用於在不執行的情況下對可疑 APK 進行靜態分類，或建構行動惡意軟體偵測規則。

使用 Apktool 分析 Android 惡意軟體 是什麼？

此技能旨在對 Android APK 檔案進行靜態分析，協助安全研究人員在不執行程式碼的情況下提取關鍵安全指標。它整合 androguard 進行程式化 APK 分析，可從 AndroidManifest.xml 中解析出權限、活動、服務、廣播接收器及內容提供者，並自動標記危險權限組合。此外，技能還會掃描混淆程式碼模式、動態程式碼載入（如 DexClassLoader）、基於反射的 API 呼叫以及硬編碼的網路指標（URL、IP、C2 伺服器）。支援的平臺包括 Python 環境（需要 androguard）以及可選的 apktool 與 jadx 工具。使用場景涵蓋安全事件調查、偵測規則建構與威脅狩獵。最終輸出為 JSON 格式報告，包含權限分析、元件清單、可疑 API、網路指標與風險評分，並可提取潛在的入侵指標（IOC）。

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說明文件

使用 Apktool 分析 Android 惡意軟體 是做什麼的？

概述

以 APK 檔案形式散佈的 Android 惡意軟體，可透過靜態分析提取權限、活動、服務、廣播接收器以及可疑的 API 呼叫，且無需執行樣本。此技能使用 androguard 進行程式化 APK 分析，識別危險的權限組合、混淆程式碼模式、動態程式碼載入、基於反射的 API 呼叫以及網路通訊指標。

使用時機

  • 調查需要分析 Android 惡意軟體的安全事件時
  • 為此領域建構偵測規則或威脅狩獵查詢時
  • SOC 分析師需要此分析類型的結構化程序時
  • 驗證相關攻擊技術的安全監控覆蓋範圍時

先決條件

  • Python 3.9+ 且安裝 androguard
  • apktool（用於資源反編譯）
  • jadx（用於 Java 原始碼恢復，可選）
  • 隔離的分析環境（VM 或沙箱）
  • 待分析的 APK 樣本檔案

步驟

  1. 使用 androguard 解析 APK 以提取清單後設資料
  2. 列舉請求的權限並標記危險組合
  3. 從清單中列出活動、服務、接收器與提供者
  4. 掃描可疑的 API 呼叫（反射、加密、SMS、電話）
  5. 偵測動態程式碼載入模式（DexClassLoader、Runtime.exec）
  6. 從字串中提取硬編碼的 URL、IP 與 C2 指標
  7. 生成風險評估報告，並對應 MITRE ATT&CK 行動威脅模型

預期輸出

  • JSON 報告，包含權限分析、元件清單、可疑 API 呼叫、網路指標與風險評分
  • 從 APK 中提取的字串與潛在 IOC

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-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,

analyzing-docker-container-forensics

Investigate compromised Docker containers by analyzing images, layers,

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