analyzing-prefetch-files-for-execution-history

分析 Prefetch 文件以还原执行历史

适用场景

• 需要确定 Windows 系统上执行过哪些程序以及执行时间时
• 在恶意软件调查期间确认可疑二进制文件是否被执行
• 在事件响应期间建立应用程序使用时间线
• 需要将程序执行与其他取证痕迹关联分析时
• 识别已运行的反取证工具或未授权软件

前提条件

• 可从取证镜像访问 Windows Prefetch 目录（C:\Windows\Prefetch\）
• PECmd（Eric Zimmerman）、WinPrefetchView 或 python-prefetch 解析器
• 了解 Prefetch 文件格式（版本 17、23、26、30）
• 已启用 Prefetch 的 Windows 系统（客户端操作系统默认启用，服务器默认禁用）
• 了解 Prefetch 命名约定（APPNAME-HASH.pf）

工作流

步骤 1：从取证镜像提取 Prefetch 文件

# Mount the forensic image
mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/evidence.dd /mnt/evidence

# Copy all prefetch files
mkdir -p /cases/case-2024-001/prefetch/
cp /mnt/evidence/Windows/Prefetch/*.pf /cases/case-2024-001/prefetch/

# Count and list prefetch files
ls -la /cases/case-2024-001/prefetch/ | wc -l
ls -la /cases/case-2024-001/prefetch/ | head -30

# Hash all prefetch files for integrity
sha256sum /cases/case-2024-001/prefetch/*.pf > /cases/case-2024-001/prefetch/pf_hashes.txt

# Note: Prefetch filename format is EXECUTABLE_NAME-XXXXXXXX.pf
# The hash (XXXXXXXX) is based on the executable path
# Same executable from different paths creates different prefetch files

步骤 2：使用 PECmd 解析 Prefetch 文件

# Using Eric Zimmerman's PECmd (Windows or via Mono/Wine on Linux)
# Download from https://ericzimmerman.github.io/

# Parse a single prefetch file
PECmd.exe -f "C:\cases\prefetch\POWERSHELL.EXE-A]B2C3D4.pf"

# Parse all prefetch files and output to CSV
PECmd.exe -d "C:\cases\prefetch\" --csv "C:\cases\analysis\" --csvf prefetch_results.csv

# Parse with JSON output
PECmd.exe -d "C:\cases\prefetch\" --json "C:\cases\analysis\" --jsonf prefetch_results.json

# Output includes for each file:
# - Executable name and path
# - Run count
# - Last run time (up to 8 timestamps in Windows 10)
# - Files and directories referenced during execution
# - Volume information (serial number, creation date)
# - Prefetch file creation time

步骤 3：使用 Python 进行基于 Linux 的分析

pip install prefetch

python3 << 'PYEOF'
import os
import json
from datetime import datetime

# Parse prefetch files using python
import struct

def parse_prefetch(filepath):
    """Parse a Windows Prefetch file."""
    with open(filepath, 'rb') as f:
        data = f.read()

    # Check for MAM compressed format (Windows 10)
    if data[:4] == b'MAM\x04':
        import lznt1  # or use DecompressBuffer
        # Windows 10 prefetch files are compressed
        print(f"  [Compressed Win10 format - use PECmd for full parsing]")
        return None

    # Version 17 (XP), 23 (Vista/7), 26 (8.1), 30 (10)
    version = struct.unpack('<I', data[0:4])[0]
    signature = data[4:8]

    if signature != b'SCCA':
        print(f"  Invalid prefetch signature")
        return None

    file_size = struct.unpack('<I', data[8:12])[0]
    exec_name = data[16:76].decode('utf-16-le').strip('\x00')
    run_count = struct.unpack('<I', data[208:212])[0] if version >= 23 else struct.unpack('<I', data[144:148])[0]

    result = {
        'version': version,
        'executable': exec_name,
        'file_size': file_size,
        'run_count': run_count,
    }

    # Extract last execution timestamps
    if version == 23:  # Vista/7 - 1 timestamp
        ts = struct.unpack('<Q', data[128:136])[0]
        result['last_run'] = filetime_to_datetime(ts)
    elif version >= 26:  # Win8+ - up to 8 timestamps
        timestamps = []
        for i in range(8):
            ts = struct.unpack('<Q', data[128+i*8:136+i*8])[0]
            if ts > 0:
                timestamps.append(filetime_to_datetime(ts))
        result['last_run_times'] = timestamps

    return result

def filetime_to_datetime(ft):
    """Convert Windows FILETIME to datetime string."""
    if ft == 0:
        return None
    timestamp = (ft - 116444736000000000) / 10000000
    try:
        return datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S UTC')
    except (OSError, ValueError):
        return None

# Process all prefetch files
prefetch_dir = '/cases/case-2024-001/prefetch/'
results = []

for filename in sorted(os.listdir(prefetch_dir)):
    if filename.lower().endswith('.pf'):
        filepath = os.path.join(prefetch_dir, filename)
        print(f"\n=== {filename} ===")
        result = parse_prefetch(filepath)
        if result:
            print(f"  Executable: {result['executable']}")
            print(f"  Run Count:  {result['run_count']}")
            if 'last_run' in result:
                print(f"  Last Run:   {result['last_run']}")
            elif 'last_run_times' in result:
                for i, ts in enumerate(result['last_run_times']):
                    print(f"  Run Time {i+1}: {ts}")
            results.append(result)

# Save results
with open('/cases/case-2024-001/analysis/prefetch_analysis.json', 'w') as f:
    json.dump(results, f, indent=2)
PYEOF

步骤 4：识别可疑执行证据

# Search for known malicious tool names in prefetch
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(MIMIKATZ|PSEXEC|WMIC|COBALT|BEACON|PWDUMP|PROCDUMP|LAZAGNE|RUBEUS|BLOODHOUND|SHARPHOUND|CERTUTIL|BITSADMIN)'

# Search for script interpreters (potential malicious execution)
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(POWERSHELL|CMD\.EXE|WSCRIPT|CSCRIPT|MSHTA|REGSVR32|RUNDLL32|MSIEXEC)'

# Search for remote access tools
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(TEAMVIEWER|ANYDESK|LOGMEIN|VNC|SPLASHTOP|SCREENCONNECT|AMMYY)'

# Search for data exfiltration tools
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(RAR|7Z|ZIP|RCLONE|MEGA|DROPBOX|ONEDRIVE|GDRIVE|FTP|CURL|WGET)'

# Find recently created prefetch files (newest executables run)
ls -lt /cases/case-2024-001/prefetch/ | head -20

# Cross-reference with Shimcache and Amcache for confirmation
# Prefetch existence = program was executed at least once

步骤 5：构建执行时间线

# Create timeline from prefetch data
python3 << 'PYEOF'
import json
import csv

with open('/cases/case-2024-001/analysis/prefetch_analysis.json') as f:
    data = json.load(f)

timeline = []
for entry in data:
    if 'last_run_times' in entry:
        for ts in entry['last_run_times']:
            if ts:
                timeline.append({
                    'timestamp': ts,
                    'executable': entry['executable'],
                    'run_count': entry['run_count'],
                    'source': 'Prefetch'
                })
    elif 'last_run' in entry and entry['last_run']:
        timeline.append({
            'timestamp': entry['last_run'],
            'executable': entry['executable'],
            'run_count': entry['run_count'],
            'source': 'Prefetch'
        })

# Sort chronologically
timeline.sort(key=lambda x: x['timestamp'])

# Write timeline CSV
with open('/cases/case-2024-001/analysis/execution_timeline.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['timestamp', 'executable', 'run_count', 'source'])
    writer.writeheader()
    writer.writerows(timeline)

# Print suspicious time window
for entry in timeline:
    if '2024-01-15' in entry['timestamp'] or '2024-01-16' in entry['timestamp']:
        print(f"  {entry['timestamp']} | {entry['executable']} (x{entry['run_count']})")
PYEOF

关键概念

概念	描述
Prefetch	Windows 性能优化机制，预加载应用程序数据并跟踪执行情况
SCCA 签名	用于标识有效 Prefetch 文件的魔数字节
路径哈希	基于可执行文件路径的 CRC 哈希，构成 .pf 文件名的一部分
运行次数	可执行文件被启动的次数（可能会回绕）
上次运行时间戳	Windows 8+ 最多存储 8 个最近的执行时间戳
引用文件	执行前 10 秒内访问的文件和目录列表
卷信息	标识源卷的驱动器序列号和创建日期
MAM 压缩	Windows 10 Prefetch 文件使用 MAM4 压缩，解析前需要解压缩

工具与系统

工具	用途
PECmd	Eric Zimmerman 的 Prefetch 解析器，支持 CSV/JSON 输出
WinPrefetchView	NirSoft 的 GUI 工具，用于查看 Prefetch 文件
python-prefetch	用于解析 Prefetch 文件的 Python 库
Prefetch Hash Calculator	根据可执行文件路径计算预期哈希的工具
KAPE	自动化痕迹收集工具，包括 Prefetch
Autopsy	带有 Prefetch 分析模块的取证平台
Plaso/log2timeline	包含 Prefetch 解析器的超级时间线工具
Velociraptor	带有 Prefetch 收集和分析构件的终端 Agent

常见场景

场景 1：确认恶意软件执行 在 Prefetch 目录中搜索恶意软件可执行文件名称，通过 Prefetch 的存在确认执行，提取运行次数和上次运行时间，识别引用的 DLL 以了解恶意软件行为，与注册表自启动项关联分析。

场景 2：攻击者工具使用时间线 识别 PsExec、Mimikatz、BloodHound 及其他攻击者工具的 Prefetch 文件，构建工具执行的时间顺序时间线，确定攻击序列（侦察、凭据窃取、横向移动），将时间戳与网络连接日志匹配。

场景 3：数据暂存与外泄 查找压缩工具（7z、WinRAR、zip）的 Prefetch 条目，识别文件传输工具（rclone、FTP 客户端）的执行，检查云存储客户端的执行情况，梳理数据暂存和传输发生的时间线。

场景 4：反取证检测 检查已知反取证工具（CCleaner、Eraser、SDelete）的执行情况，识别 Prefetch 目录是否最近被清除（文件数量少于活跃系统的预期值），记录反取证工具执行时间戳与其他证据的相对关系。

输出格式

Prefetch Analysis Summary:
  System: Windows 10 Pro (Build 19041)
  Prefetch Files: 234
  Analysis Period: All available execution history

  Execution Statistics:
    Total unique executables: 234
    First execution: 2023-06-15 (system install)
    Latest execution: 2024-01-18 23:45 UTC

  Suspicious Executions:
    MIMIKATZ.EXE-5F2A3B1C.pf
      Run Count: 3 | Last: 2024-01-16 02:30:15 UTC
    PSEXEC.EXE-AD70946C.pf
      Run Count: 7 | Last: 2024-01-16 02:45:30 UTC
    RCLONE.EXE-1F3E5A2B.pf
      Run Count: 2 | Last: 2024-01-17 03:15:00 UTC
    POWERSHELL.EXE-022A1004.pf
      Run Count: 145 | Last: 2024-01-18 14:00:00 UTC

  Attack Timeline (from Prefetch):
    2024-01-15 14:32 - POWERSHELL.EXE (initial access)
    2024-01-16 02:30 - MIMIKATZ.EXE (credential theft)
    2024-01-16 02:45 - PSEXEC.EXE (lateral movement)
    2024-01-17 03:15 - RCLONE.EXE (data exfiltration)

  Report: /cases/case-2024-001/analysis/execution_timeline.csv