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使用 Python 分析 Windows Prefetch

使用 windowsprefetch Python 函式庫解析 Windows Prefetch (.pf) 檔案,還原執行歷程、偵測可疑程式。

使用 Python 分析 Windows Prefetch 是什麼?

此技能教導如何利用 Python 的 `windowsprefetch` 函式庫解析 Windows Prefetch 檔案(.pf)。Prefetch 檔案會記錄應用程式的執行名稱、執行次數、時間戳記、載入的 DLL 及存取的目錄。透過解析這些資訊,您可以重建執行時間線、比對執行檔名稱與實際載入的資源來偵測改名或偽裝的木馬,並找出可能暗示惡意程式執行或橫向移動的可疑軟體。本技能適用於任何支援 Python 的安全分析環境,包含 Cursor 與 Claude Code,並提供完整的實作步驟、預期輸出與範例報告,協助 SOC 分析師快速建立威脅獵捕規則或驗證監控覆蓋率。

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說明文件

使用 Python 分析 Windows Prefetch 是做什麼的?

概述

Windows Prefetch 檔案 (.pf) 記錄應用程式的執行資料,包括可執行檔名稱、執行次數、時間戳記、載入的 DLL 以及存取的目錄。本技能涵蓋使用 windowsprefetch Python 函式庫解析 Prefetch 檔案,以重建執行時間線、透過比較執行檔名稱與載入的資源來偵測改名或偽裝的二進位檔案,以及識別可能表示惡意程式執行或橫向移動的可疑程式。

使用時機

  • 調查安全事件時,需要分析 Windows Prefetch 檔案 (使用 Python)
  • 為此領域建立偵測規則或威脅獵捕查詢時
  • 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監控覆蓋率時

先決條件

  • Python 3.9+ 並安裝 windowsprefetch 函式庫 (pip install windowsprefetch)
  • 來自 C:\Windows\Prefetch\ 的 Windows Prefetch 檔案 (支援版本 17 至 30)
  • 了解 Windows Prefetch 檔案命名慣例 (EXECUTABLE-HASH.pf)

步驟

步驟 1:收集 Prefetch 檔案

從目標系統的 C:\Windows\Prefetch\ 目錄收集 .pf 檔案。

步驟 2:解析執行歷史記錄

擷取可執行檔名稱、執行次數、最後執行時間戳記與磁碟區資訊。

步驟 3:偵測可疑執行

標記已知的攻擊工具 (mimikatz、psexec 等)、經改名的二進位檔案以及異常的執行模式。

步驟 4:建立執行時間線

從所有 Prefetch 檔案重建按時間排序的執行時間線。

預期輸出

JSON 格式的報告,內容包含執行歷史記錄、可疑的可執行檔、改名二進位檔案指標以及時間線重建結果。

範例輸出

$ python3 prefetch_analyzer.py --dir /evidence/Windows/Prefetch --output /analysis/prefetch_report

Windows Prefetch Analyzer v2.1
================================
來源: /evidence/Windows/Prefetch/
Prefetch 格式: Windows 10 (MAM 壓縮,版本 30)
找到的檔案數: 234

--- 執行時間線 (事件窗口: 2024-01-15 至 2024-01-18) ---
最後執行時間 (UTC)    | 執行次數 | 檔案名稱                     | 雜湊值   | 路徑
------------------------|-----------|------------------------------|----------|------------------------------------------
2024-01-15 14:33:15     | 1         | Q4_REPORT.XLSM-2A1B3C4D.pf  | 2A1B3C4D | C:\Users\jsmith\Downloads\Q4_Report.xlsm
2024-01-15 14:35:44     | 1         | POWERSHELL.EXE-A2B3C4D5.pf  | A2B3C4D5 | C:\Windows\System32\WindowsPowerShell\v1.0\powershell.exe
2024-01-15 14:36:30     | 3         | UPDATE_CLIENT.EXE-B3C4D5E6.pf| B3C4D5E6| C:\ProgramData\Updates\update_client.exe
2024-01-15 15:10:22     | 1         | NETSCAN.EXE-C4D5E6F7.pf     | C4D5E6F7 | C:\Users\jsmith\Downloads\netscan.exe
2024-01-16 02:28:00     | 1         | PROCDUMP64.EXE-D5E6F7A8.pf  | D5E6F7A8 | C:\Windows\Temp\procdump64.exe
2024-01-16 02:30:15     | 2         | MIMIKATZ.EXE-E6F7A8B9.pf    | E6F7A8B9 | C:\Windows\Temp\mimikatz.exe
2024-01-16 02:40:00     | 4         | PSEXEC.EXE-F7A8B9C0.pf      | F7A8B9C0 | C:\Users\jsmith\AppData\Local\Temp\psexec.exe
2024-01-17 02:45:00     | 1         | SDELETE64.EXE-A8B9C0D1.pf   | A8B9C0D1 | C:\Windows\Temp\sdelete64.exe
2024-01-18 03:00:45     | 1         | WEVTUTIL.EXE-B9C0D1E2.pf    | B9C0D1E2 | C:\Windows\System32\wevtutil.exe

--- 改名二進位檔案偵測 ---
警告: UPDATE_CLIENT.EXE 載入的 DLL 與 Cobalt Strike 信標一致:
  參考的 DLL: wininet.dll, ws2_32.dll, advapi32.dll, dnsapi.dll, netapi32.dll
  磁碟區: \VOLUME{01d94f2a3b5c7d8e-A4E73F21} (C:)
  參考的目錄:
    C:\ProgramData\Updates\
    C:\Windows\System32\

--- 執行頻率分析 ---
最多執行 (前 5 名):
  1. SVCHOST.EXE          (267 次)
  2. CHROME.EXE           (189 次)
  3. EXPLORER.EXE         (156 次)
  4. RUNTIMEBROKER.EXE    (134 次)
  5. OUTLOOK.EXE          (98 次)

首次執行 (事件窗口前從未出現):
  6 個可執行檔於 2024-01-15 至 2024-01-18 之間首次執行

摘要:
  總計 Prefetch 檔案數:         234
  可疑可執行檔:                 6
  改名二進位檔案指標:           1 (update_client.exe)
  反鑑識工具:                   2 (sdelete64.exe, wevtutil.exe)
  JSON 報告: /analysis/prefetch_report/prefetch_timeline.json

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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