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分析 Office 365 稽核記錄以檢測入侵

透過 Microsoft Graph API 解析 Office 365 整合稽核記錄，以檢測商業電子郵件入侵 (BEC) 的跡象。

分析 Office 365 稽核記錄以檢測入侵 是什麼？

此技能專注於使用 Microsoft Graph API 分析 Office 365 整合稽核記錄，以偵測商業電子郵件入侵 (BEC) 攻擊的常見跡象。其核心價值在於自動化調查可疑的收件匣規則建立、電子郵件轉寄至外部地址、信箱委派變更及未經授權的 OAuth 應用程式同意授權等事件。此技能適用於安全營運中心 (SOC) 分析師進行威脅狩獵、事件調查，以及驗證安全監控覆蓋範圍。它支援 Python 環境，並需透過 Azure AD 應用程式註冊取得相關 Graph API 權限（如 AuditLog.Read.All、MailboxSettings.Read），最終會產出包含風險評分的結構化 JSON 報告與入侵指標時間軸，有效提升調查效率與準確性。

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說明文件

分析 Office 365 稽核記錄以檢測入侵 是做什麼的？

概述

商業電子郵件入侵（BEC）攻擊通常會在 Office 365 稽核記錄中留下痕跡：可疑的收件匣規則建立、電子郵件轉寄至外部地址、信箱委派變更，以及未經授權的 OAuth 應用程式同意授權。此技能使用 Microsoft Graph API 查詢整合稽核記錄、列舉跨信箱的收件匣規則、偵測轉寄設定，並識別受入侵帳戶的指標。

使用時機

  • 當調查安全事件需要分析 office365 稽核記錄以檢測入侵時
  • 當為此領域建立偵測規則或威脅狩獵查詢時
  • 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

  • 具有 AuditLog.Read.All、MailboxSettings.Read、Mail.Read（應用程式權限）的 Azure AD 應用程式註冊
  • Python 3.9+ 搭配 msal、requests
  • 用於驗證的用戶端密碼或憑證
  • 全域讀取者或安全性讀取者角色

步驟

  1. 使用 MSAL 用戶端憑證流程向 Microsoft Graph 進行驗證
  2. 查詢整合稽核記錄中可疑的操作（Set-Mailbox、New-InboxRule）
  3. 列舉跨信箱的收件匣規則，並標記轉寄規則
  4. 偵測信箱委派變更（Add-MailboxPermission）
  5. 識別對可疑應用程式的 OAuth 同意授權
  6. 從稽核記錄中檢查可疑的登入模式
  7. 產生包含時間軸的入侵指標報告

預期輸出

  • JSON 報告，列出轉寄規則、委派變更、OAuth 授權以及帶有風險評分的可疑稽核事件
  • 包含受影響信箱的入侵指標時間軸

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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