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静态分析Android恶意软件(使用Apktool)

使用apktool进行资源反编译、jadx进行Java源码恢复、androguard进行清单检查、危险权限组合检测以及识别混淆代码、动态代码加载和基于反射的API调用,对Android APK恶意软件执行静态分析。可用于在不执行可疑APK的情况下进行静态分类,或构建移动恶意软件检测规则。

静态分析Android恶意软件(使用Apktool) 是什么?

该技能专注于对Android APK恶意软件进行静态分析,无需执行样本即可提取权限、活动、服务、广播接收器和可疑API调用。它利用androguard进行程序化APK分析,识别危险权限组合、混淆代码模式、动态代码加载、基于反射的API调用以及网络通信指标。技能提供了一套结构化步骤,包括使用apktool进行资源反编译、jadx进行Java源码恢复,以及androguard进行清单检查和恶意行为检测。最终输出包含权限分析、组件列表、可疑API调用、网络指标和风险评分,并映射到MITRE ATT&CK移动攻击技术。该技能适用于安全事件调查、检测规则构建、威胁狩猎和SOC分析。

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文档

使用Apktool分析Android恶意软件

概述

以APK文件形式分发的Android恶意软件可以通过静态分析提取权限、活动、服务、广播接收器和可疑API调用,而无需执行样本。本技能使用androguard进行程序化APK分析,识别危险权限组合、混淆代码模式、动态代码加载、基于反射的API调用以及网络通信指标。

何时使用

  • 调查安全事件时,需要使用apktool分析Android恶意软件
  • 为该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

前提条件

  • Python 3.9+ 并安装 androguard
  • apktool(用于资源反编译)
  • jadx(用于Java源码恢复,可选)
  • 隔离的分析环境(虚拟机或沙箱)
  • 待分析的APK样本文件

步骤

  1. 使用androguard解析APK,提取清单元数据
  2. 枚举请求的权限,并标记危险组合
  3. 从清单中列出活动、服务、接收器和提供者
  4. 扫描可疑API调用(反射、加密、短信、电话)
  5. 检测动态代码加载模式(DexClassLoader、Runtime.exec)
  6. 从字符串中提取硬编码的URL、IP和C2指标
  7. 生成风险评估报告,并映射MITRE ATT&CK移动攻击技术

预期输出

  • 包含权限分析、组件列表、可疑API调用、网络指标和风险评分的JSON报告
  • 从APK中提取的字符串和潜在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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