Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Windows Prefetch files (.pf) record application execution data including executable names, run counts, timestamps, loaded DLLs, and accessed directories. This skill covers parsing Prefetch files using the windowsprefetch Python library to reconstruct execution timelines, detect renamed or masquerading binaries by comparing executable names with loaded resources, and identifying suspicious programs that may indicate malware execution or lateral movement.
windowsprefetch library (pip install windowsprefetch)Gather .pf files from target system's C:\Windows\Prefetch\ directory.
Extract executable name, run count, last execution timestamps, and volume information.
Flag known attack tools (mimikatz, psexec, etc.), renamed binaries, and unusual execution patterns.
Reconstruct chronological execution timeline from all Prefetch files.
JSON report with execution history, suspicious executables, renamed binary indicators, and timeline reconstruction.
$ python3 prefetch_analyzer.py --dir /evidence/Windows/Prefetch --output /analysis/prefetch_report
Windows Prefetch Analyzer v2.1
================================
Source: /evidence/Windows/Prefetch/
Prefetch Format: Windows 10 (MAM compressed, version 30)
Files Found: 234
--- Execution Timeline (Incident Window: 2024-01-15 to 2024-01-18) ---
Last Executed (UTC) | Run Count | Filename | Hash | Path
------------------------|-----------|-----------------------------|----------|------------------------------------------
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
--- Renamed Binary Detection ---
ALERT: UPDATE_CLIENT.EXE loaded DLLs consistent with Cobalt Strike beacon:
Referenced DLLs: wininet.dll, ws2_32.dll, advapi32.dll, dnsapi.dll, netapi32.dll
Volume: \VOLUME{01d94f2a3b5c7d8e-A4E73F21} (C:)
Directories referenced:
C:\ProgramData\Updates\
C:\Windows\System32\
--- Execution Frequency Analysis ---
Most Executed (Top 5):
1. SVCHOST.EXE (267 runs)
2. CHROME.EXE (189 runs)
3. EXPLORER.EXE (156 runs)
4. RUNTIMEBROKER.EXE (134 runs)
5. OUTLOOK.EXE (98 runs)
First-Time Executions (Never seen before incident window):
6 executables first run between 2024-01-15 and 2024-01-18
Summary:
Total prefetch files: 234
Suspicious executables: 6
Renamed binary indicators: 1 (update_client.exe)
Anti-forensics tools: 2 (sdelete64.exe, wevtutil.exe)
JSON report: /analysis/prefetch_report/prefetch_timeline.json
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
日本語の概要は準備中です。原文の説明を表示しています。
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
日本語の概要は準備中です。原文の説明を表示しています。
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
日本語の概要は準備中です。原文の説明を表示しています。