analyzing-slack-space-and-file-system-artifacts

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

Analyzing Slack Space and File System Artifacts

分析松弛空间与文件系统取证痕迹

When to Use

适用场景

  • When searching for hidden or residual data in file system slack space
  • For analyzing NTFS Master File Table (MFT) entries for deleted file metadata
  • When reconstructing file operations from the USN Change Journal
  • For detecting Alternate Data Streams (ADS) used to hide data or malware
  • During deep forensic analysis requiring examination beyond standard file recovery
  • 在文件系统松弛空间中搜索隐藏或残留数据时
  • 分析NTFS主文件表(MFT)条目以获取已删除文件的元数据时
  • 从USN变更日志重建文件操作时
  • 检测用于隐藏数据或恶意软件的备用数据流(ADS)时
  • 需要进行超出标准文件恢复的深度取证分析时

Prerequisites

前置条件

  • Forensic disk image with NTFS file system
  • The Sleuth Kit (TSK) tools: istat, icat, fls, blkls, blkstat
  • MFTECmd (Eric Zimmerman) for MFT parsing
  • MFTExplorer for interactive MFT analysis
  • Understanding of NTFS structures (MFT, $UsnJrnl, $LogFile, ADS)
  • Python with analyzeMFT or mft library for automated parsing
  • 包含NTFS文件系统的取证磁盘镜像
  • The Sleuth Kit(TSK)工具:istat、icat、fls、blkls、blkstat
  • 用于MFT解析的MFTECmd(Eric Zimmerman开发)
  • 用于交互式MFT分析的MFTExplorer
  • 了解NTFS结构(MFT、$UsnJrnl、$LogFile、ADS)
  • 安装有analyzeMFT或mft库的Python环境,用于自动化解析

Workflow

工作流程

Step 1: Identify and Extract NTFS File System Artifacts

步骤1:识别并提取NTFS文件系统取证痕迹

bash
undefined
bash
undefined

Determine partition layout

Determine partition layout

mmls /cases/case-2024-001/images/evidence.dd
mmls /cases/case-2024-001/images/evidence.dd

Extract key NTFS system files

Extract key NTFS system files

$MFT - Master File Table

$MFT - Master File Table

icat -o 2048 /cases/case-2024-001/images/evidence.dd 0 > /cases/case-2024-001/ntfs/MFT
icat -o 2048 /cases/case-2024-001/images/evidence.dd 0 > /cases/case-2024-001/ntfs/MFT

$UsnJrnl:$J - USN Change Journal

$UsnJrnl:$J - USN Change Journal

icat -o 2048 /cases/case-2024-001/images/evidence.dd 62-128 > /cases/case-2024-001/ntfs/UsnJrnl_J
icat -o 2048 /cases/case-2024-001/images/evidence.dd 62-128 > /cases/case-2024-001/ntfs/UsnJrnl_J

$LogFile - Transaction log

$LogFile - Transaction log

icat -o 2048 /cases/case-2024-001/images/evidence.dd 2 > /cases/case-2024-001/ntfs/LogFile
icat -o 2048 /cases/case-2024-001/images/evidence.dd 2 > /cases/case-2024-001/ntfs/LogFile

Extract all slack space from the volume

Extract all slack space from the volume

blkls -s -o 2048 /cases/case-2024-001/images/evidence.dd > /cases/case-2024-001/ntfs/slack_space.raw
blkls -s -o 2048 /cases/case-2024-001/images/evidence.dd > /cases/case-2024-001/ntfs/slack_space.raw

Get file system information

Get file system information

fsstat -o 2048 /cases/case-2024-001/images/evidence.dd | tee /cases/case-2024-001/ntfs/fs_info.txt
undefined
fsstat -o 2048 /cases/case-2024-001/images/evidence.dd | tee /cases/case-2024-001/ntfs/fs_info.txt
undefined

Step 2: Analyze the Master File Table (MFT)

步骤2:分析主文件表(MFT)

bash
undefined
bash
undefined

Parse MFT with MFTECmd (Eric Zimmerman)

Parse MFT with MFTECmd (Eric Zimmerman)

MFTECmd.exe -f "C:\cases\ntfs\MFT" --csv "C:\cases\analysis" --csvf mft_analysis.csv
MFTECmd.exe -f "C:\cases\ntfs\MFT" --csv "C:\cases\analysis" --csvf mft_analysis.csv

Parse with analyzeMFT (Python)

Parse with analyzeMFT (Python)

pip install analyzeMFT
analyzeMFT.py -f /cases/case-2024-001/ntfs/MFT
-o /cases/case-2024-001/analysis/mft_analysis.csv
-c
pip install analyzeMFT
analyzeMFT.py -f /cases/case-2024-001/ntfs/MFT
-o /cases/case-2024-001/analysis/mft_analysis.csv
-c

Custom MFT analysis with Python

Custom MFT analysis with Python

python3 << 'PYEOF' from mft import PyMft import csv
mft = PyMft(open('/cases/case-2024-001/ntfs/MFT', 'rb').read())
deleted_files = [] suspicious_files = []
for entry in mft.entries(): if entry is None: continue
filename = entry.get_filename()
if filename is None:
    continue

is_deleted = not entry.is_active()
is_directory = entry.is_directory()
created = entry.get_created_timestamp()
modified = entry.get_modified_timestamp()
mft_modified = entry.get_mft_modified_timestamp()
size = entry.get_file_size()

# Flag deleted files for recovery
if is_deleted and not is_directory and size > 0:
    deleted_files.append({
        'filename': filename,
        'size': size,
        'created': str(created),
        'modified': str(modified),
        'entry_number': entry.entry_number
    })

# Detect timestomping (MFT modified time != $SI modified time)
si_modified = entry.get_si_modified_timestamp()
fn_modified = entry.get_fn_modified_timestamp()
if si_modified and fn_modified:
    if abs((si_modified - fn_modified).total_seconds()) > 86400:  # >1 day difference
        suspicious_files.append({
            'filename': filename,
            'si_modified': str(si_modified),
            'fn_modified': str(fn_modified),
            'delta': str(si_modified - fn_modified)
        })
print(f"=== DELETED FILES (recoverable metadata) ===") print(f"Total: {len(deleted_files)}") for f in deleted_files[:20]: print(f" [{f['modified']}] {f['filename']} ({f['size']} bytes)")
print(f"\n=== POTENTIAL TIMESTOMPING ===") print(f"Total suspicious: {len(suspicious_files)}") for f in suspicious_files[:10]: print(f" {f['filename']}: $SI={f['si_modified']}, $FN={f['fn_modified']} (delta: {f['delta']})") PYEOF
undefined
python3 << 'PYEOF' from mft import PyMft import csv
mft = PyMft(open('/cases/case-2024-001/ntfs/MFT', 'rb').read())
deleted_files = [] suspicious_files = []
for entry in mft.entries(): if entry is None: continue
filename = entry.get_filename()
if filename is None:
    continue

is_deleted = not entry.is_active()
is_directory = entry.is_directory()
created = entry.get_created_timestamp()
modified = entry.get_modified_timestamp()
mft_modified = entry.get_mft_modified_timestamp()
size = entry.get_file_size()

# Flag deleted files for recovery
if is_deleted and not is_directory and size > 0:
    deleted_files.append({
        'filename': filename,
        'size': size,
        'created': str(created),
        'modified': str(modified),
        'entry_number': entry.entry_number
    })

# Detect timestomping (MFT modified time != $SI modified time)
si_modified = entry.get_si_modified_timestamp()
fn_modified = entry.get_fn_modified_timestamp()
if si_modified and fn_modified:
    if abs((si_modified - fn_modified).total_seconds()) > 86400:  # >1 day difference
        suspicious_files.append({
            'filename': filename,
            'si_modified': str(si_modified),
            'fn_modified': str(fn_modified),
            'delta': str(si_modified - fn_modified)
        })
print(f"=== DELETED FILES (recoverable metadata) ===") print(f"Total: {len(deleted_files)}") for f in deleted_files[:20]: print(f" [{f['modified']}] {f['filename']} ({f['size']} bytes)")
print(f"\n=== POTENTIAL TIMESTOMPING ===") print(f"Total suspicious: {len(suspicious_files)}") for f in suspicious_files[:10]: print(f" {f['filename']}: $SI={f['si_modified']}, $FN={f['fn_modified']} (delta: {f['delta']})") PYEOF
undefined

Step 3: Analyze Slack Space for Hidden Data

步骤3:分析松弛空间中的隐藏数据

bash
undefined
bash
undefined

Search slack space for strings

Search slack space for strings

strings -a /cases/case-2024-001/ntfs/slack_space.raw > /cases/case-2024-001/analysis/slack_strings.txt
strings -a /cases/case-2024-001/ntfs/slack_space.raw > /cases/case-2024-001/analysis/slack_strings.txt

Search for specific patterns in slack space

Search for specific patterns in slack space

grep -iab "password|secret|confidential|credit.card|ssn"
/cases/case-2024-001/ntfs/slack_space.raw > /cases/case-2024-001/analysis/slack_keywords.txt
grep -iab "password|secret|confidential|credit.card|ssn"
/cases/case-2024-001/ntfs/slack_space.raw > /cases/case-2024-001/analysis/slack_keywords.txt

Analyze individual file slack

Analyze individual file slack

python3 << 'PYEOF' import struct
python3 << 'PYEOF' import struct

File slack consists of:

File slack consists of:

1. RAM slack: bytes between file end and next sector boundary (filled with RAM content or zeros)

1. RAM slack: bytes between file end and next sector boundary (filled with RAM content or zeros)

2. Drive slack: remaining sectors in the cluster after the last file sector

2. Drive slack: remaining sectors in the cluster after the last file sector

Analyze slack for specific MFT entries

Analyze slack for specific MFT entries

Using Sleuth Kit to get file slack for a specific file

Using Sleuth Kit to get file slack for a specific file

import subprocess
import subprocess

Get file details

Get file details

result = subprocess.run( ['istat', '-o', '2048', '/cases/case-2024-001/images/evidence.dd', '14523'], capture_output=True, text=True ) print(result.stdout)
result = subprocess.run( ['istat', '-o', '2048', '/cases/case-2024-001/images/evidence.dd', '14523'], capture_output=True, text=True ) print(result.stdout)

The output shows data runs - the last cluster may contain slack data

The output shows data runs - the last cluster may contain slack data

Calculate slack size: (allocated_size - file_size) bytes

Calculate slack size: (allocated_size - file_size) bytes

PYEOF
PYEOF

Search for file signatures in slack space (embedded files)

Search for file signatures in slack space (embedded files)

foremost -t jpg,pdf,zip -i /cases/case-2024-001/ntfs/slack_space.raw
-o /cases/case-2024-001/carved/slack_carved/
foremost -t jpg,pdf,zip -i /cases/case-2024-001/ntfs/slack_space.raw
-o /cases/case-2024-001/carved/slack_carved/

Use bulk_extractor to find structured data in slack

Use bulk_extractor to find structured data in slack

bulk_extractor -o /cases/case-2024-001/analysis/bulk_extract/
/cases/case-2024-001/ntfs/slack_space.raw
undefined
bulk_extractor -o /cases/case-2024-001/analysis/bulk_extract/
/cases/case-2024-001/ntfs/slack_space.raw
undefined

Step 4: Parse the USN Change Journal

步骤4:解析USN变更日志

bash
undefined
bash
undefined

Parse USN Journal with MFTECmd

Parse USN Journal with MFTECmd

MFTECmd.exe -f "C:\cases\ntfs\UsnJrnl_J" --csv "C:\cases\analysis" --csvf usn_journal.csv
MFTECmd.exe -f "C:\cases\ntfs\UsnJrnl_J" --csv "C:\cases\analysis" --csvf usn_journal.csv

Python USN Journal parsing

Python USN Journal parsing

pip install pyusn
python3 << 'PYEOF' import struct import csv from datetime import datetime, timedelta
def parse_usn_record(data, offset): """Parse a single USN_RECORD_V2.""" if offset + 8 > len(data): return None, offset
record_len = struct.unpack_from('<I', data, offset)[0]
if record_len < 56 or record_len > 65536 or offset + record_len > len(data):
    return None, offset + 8

major_ver = struct.unpack_from('<H', data, offset + 4)[0]
if major_ver != 2:
    return None, offset + record_len

mft_ref = struct.unpack_from('<Q', data, offset + 8)[0] & 0xFFFFFFFFFFFF
parent_ref = struct.unpack_from('<Q', data, offset + 16)[0] & 0xFFFFFFFFFFFF
usn = struct.unpack_from('<Q', data, offset + 24)[0]
timestamp = struct.unpack_from('<Q', data, offset + 32)[0]
reason = struct.unpack_from('<I', data, offset + 40)[0]
source_info = struct.unpack_from('<I', data, offset + 44)[0]
security_id = struct.unpack_from('<I', data, offset + 48)[0]
file_attrs = struct.unpack_from('<I', data, offset + 52)[0]
filename_len = struct.unpack_from('<H', data, offset + 56)[0]
filename_off = struct.unpack_from('<H', data, offset + 58)[0]

name = data[offset + filename_off:offset + filename_off + filename_len].decode('utf-16-le', errors='ignore')

# Convert Windows FILETIME to datetime
ts = datetime(1601, 1, 1) + timedelta(microseconds=timestamp // 10)

# Decode reason flags
reasons = []
reason_flags = {
    0x01: 'DATA_OVERWRITE', 0x02: 'DATA_EXTEND', 0x04: 'DATA_TRUNCATION',
    0x10: 'NAMED_DATA_OVERWRITE', 0x20: 'NAMED_DATA_EXTEND',
    0x100: 'FILE_CREATE', 0x200: 'FILE_DELETE', 0x400: 'EA_CHANGE',
    0x800: 'SECURITY_CHANGE', 0x1000: 'RENAME_OLD_NAME', 0x2000: 'RENAME_NEW_NAME',
    0x4000: 'INDEXABLE_CHANGE', 0x8000: 'BASIC_INFO_CHANGE',
    0x10000: 'HARD_LINK_CHANGE', 0x20000: 'COMPRESSION_CHANGE',
    0x40000: 'ENCRYPTION_CHANGE', 0x80000: 'OBJECT_ID_CHANGE',
    0x100000: 'REPARSE_POINT_CHANGE', 0x200000: 'STREAM_CHANGE',
    0x80000000: 'CLOSE'
}
for flag, desc in reason_flags.items():
    if reason & flag:
        reasons.append(desc)

record = {
    'timestamp': ts.strftime('%Y-%m-%d %H:%M:%S'),
    'filename': name,
    'mft_entry': mft_ref,
    'parent_entry': parent_ref,
    'reasons': '|'.join(reasons),
    'usn': usn
}

return record, offset + record_len
pip install pyusn
python3 << 'PYEOF' import struct import csv from datetime import datetime, timedelta
def parse_usn_record(data, offset): """Parse a single USN_RECORD_V2.""" if offset + 8 > len(data): return None, offset
record_len = struct.unpack_from('<I', data, offset)[0]
if record_len < 56 or record_len > 65536 or offset + record_len > len(data):
    return None, offset + 8

major_ver = struct.unpack_from('<H', data, offset + 4)[0]
if major_ver != 2:
    return None, offset + record_len

mft_ref = struct.unpack_from('<Q', data, offset + 8)[0] & 0xFFFFFFFFFFFF
parent_ref = struct.unpack_from('<Q', data, offset + 16)[0] & 0xFFFFFFFFFFFF
usn = struct.unpack_from('<Q', data, offset + 24)[0]
timestamp = struct.unpack_from('<Q', data, offset + 32)[0]
reason = struct.unpack_from('<I', data, offset + 40)[0]
source_info = struct.unpack_from('<I', data, offset + 44)[0]
security_id = struct.unpack_from('<I', data, offset + 48)[0]
file_attrs = struct.unpack_from('<I', data, offset + 52)[0]
filename_len = struct.unpack_from('<H', data, offset + 56)[0]
filename_off = struct.unpack_from('<H', data, offset + 58)[0]

name = data[offset + filename_off:offset + filename_off + filename_len].decode('utf-16-le', errors='ignore')

# Convert Windows FILETIME to datetime
ts = datetime(1601, 1, 1) + timedelta(microseconds=timestamp // 10)

# Decode reason flags
reasons = []
reason_flags = {
    0x01: 'DATA_OVERWRITE', 0x02: 'DATA_EXTEND', 0x04: 'DATA_TRUNCATION',
    0x10: 'NAMED_DATA_OVERWRITE', 0x20: 'NAMED_DATA_EXTEND',
    0x100: 'FILE_CREATE', 0x200: 'FILE_DELETE', 0x400: 'EA_CHANGE',
    0x800: 'SECURITY_CHANGE', 0x1000: 'RENAME_OLD_NAME', 0x2000: 'RENAME_NEW_NAME',
    0x4000: 'INDEXABLE_CHANGE', 0x8000: 'BASIC_INFO_CHANGE',
    0x10000: 'HARD_LINK_CHANGE', 0x20000: 'COMPRESSION_CHANGE',
    0x40000: 'ENCRYPTION_CHANGE', 0x80000: 'OBJECT_ID_CHANGE',
    0x100000: 'REPARSE_POINT_CHANGE', 0x200000: 'STREAM_CHANGE',
    0x80000000: 'CLOSE'
}
for flag, desc in reason_flags.items():
    if reason & flag:
        reasons.append(desc)

record = {
    'timestamp': ts.strftime('%Y-%m-%d %H:%M:%S'),
    'filename': name,
    'mft_entry': mft_ref,
    'parent_entry': parent_ref,
    'reasons': '|'.join(reasons),
    'usn': usn
}

return record, offset + record_len

Parse the journal

Parse the journal

with open('/cases/case-2024-001/ntfs/UsnJrnl_J', 'rb') as f: data = f.read()
records = [] offset = 0 while offset < len(data) - 8: record, offset = parse_usn_record(data, offset) if record: records.append(record) else: offset += 8 # Skip zeros
with open('/cases/case-2024-001/ntfs/UsnJrnl_J', 'rb') as f: data = f.read()
records = [] offset = 0 while offset < len(data) - 8: record, offset = parse_usn_record(data, offset) if record: records.append(record) else: offset += 8 # Skip zeros

Filter for deletion events

Filter for deletion events

deletions = [r for r in records if 'FILE_DELETE' in r['reasons']] creations = [r for r in records if 'FILE_CREATE' in r['reasons']] renames = [r for r in records if 'RENAME_NEW_NAME' in r['reasons']]
print(f"Total USN records: {len(records)}") print(f"File creations: {len(creations)}") print(f"File deletions: {len(deletions)}") print(f"File renames: {len(renames)}")
print("\n=== RECENT DELETIONS ===") for r in deletions[-20:]: print(f" [{r['timestamp']}] DELETED: {r['filename']} (MFT#{r['mft_entry']})")
deletions = [r for r in records if 'FILE_DELETE' in r['reasons']] creations = [r for r in records if 'FILE_CREATE' in r['reasons']] renames = [r for r in records if 'RENAME_NEW_NAME' in r['reasons']]
print(f"Total USN records: {len(records)}") print(f"File creations: {len(creations)}") print(f"File deletions: {len(deletions)}") print(f"File renames: {len(renames)}")
print("\n=== RECENT DELETIONS ===") for r in deletions[-20:]: print(f" [{r['timestamp']}] DELETED: {r['filename']} (MFT#{r['mft_entry']})")

Write full journal to CSV

Write full journal to CSV

with open('/cases/case-2024-001/analysis/usn_journal.csv', 'w', newline='') as f: writer = csv.DictWriter(f, fieldnames=['timestamp', 'filename', 'mft_entry', 'parent_entry', 'reasons', 'usn']) writer.writeheader() writer.writerows(records) PYEOF
undefined
with open('/cases/case-2024-001/analysis/usn_journal.csv', 'w', newline='') as f: writer = csv.DictWriter(f, fieldnames=['timestamp', 'filename', 'mft_entry', 'parent_entry', 'reasons', 'usn']) writer.writeheader() writer.writerows(records) PYEOF
undefined

Step 5: Detect and Analyze Alternate Data Streams

步骤5:检测并分析备用数据流(ADS)

bash
undefined
bash
undefined

List all Alternate Data Streams in the image

List all Alternate Data Streams in the image

find /mnt/evidence -exec getfattr -d {} ; 2>/dev/null | grep -i "ads|zone|stream"
find /mnt/evidence -exec getfattr -d {} ; 2>/dev/null | grep -i "ads|zone|stream"

Using Sleuth Kit to find ADS

Using Sleuth Kit to find ADS

fls -r -o 2048 /cases/case-2024-001/images/evidence.dd | grep ":" |
tee /cases/case-2024-001/analysis/ads_list.txt
fls -r -o 2048 /cases/case-2024-001/images/evidence.dd | grep ":" |
tee /cases/case-2024-001/analysis/ads_list.txt

Extract specific ADS content

Extract specific ADS content

Format: icat image inode:ads_name

Format: icat image inode:ads_name

icat -o 2048 /cases/case-2024-001/images/evidence.dd 14523:hidden_stream \
/cases/case-2024-001/analysis/extracted_ads.bin
icat -o 2048 /cases/case-2024-001/images/evidence.dd 14523:hidden_stream \
/cases/case-2024-001/analysis/extracted_ads.bin

Check Zone.Identifier streams (download origin tracking)

Check Zone.Identifier streams (download origin tracking)

fls -r -o 2048 /cases/case-2024-001/images/evidence.dd | grep "Zone.Identifier" |
while read line; do inode=$(echo "$line" | awk '{print $2}' | tr -d ':') echo "=== $line ===" icat -o 2048 /cases/case-2024-001/images/evidence.dd "${inode}:Zone.Identifier" 2>/dev/null echo "" done > /cases/case-2024-001/analysis/zone_identifiers.txt
fls -r -o 2048 /cases/case-2024-001/images/evidence.dd | grep "Zone.Identifier" |
while read line; do inode=$(echo "$line" | awk '{print $2}' | tr -d ':') echo "=== $line ===" icat -o 2048 /cases/case-2024-001/images/evidence.dd "${inode}:Zone.Identifier" 2>/dev/null echo "" done > /cases/case-2024-001/analysis/zone_identifiers.txt

Zone.Identifier content reveals:

Zone.Identifier content reveals:

[ZoneTransfer]

[ZoneTransfer]

ZoneId=3 (3 = Internet, indicating file was downloaded)

ZoneId=3 (3 = Internet, indicating file was downloaded)

undefined
undefined

Key Concepts

核心概念

ConceptDescription
File slackUnused space between file end and cluster boundary containing residual data
RAM slackPortion of slack from file end to sector boundary (historically filled with RAM)
MFT ($MFT)Master File Table - NTFS metadata database with entries for every file
USN Journal ($UsnJrnl)Change journal recording all file/directory modifications on NTFS
Alternate Data StreamsNTFS feature allowing multiple data streams per file (hidden storage)
$STANDARD_INFORMATIONMFT attribute with timestamps modifiable by user-mode applications
$FILE_NAMEMFT attribute with timestamps only modifiable by the kernel
TimestompingAnti-forensic technique modifying file timestamps to avoid detection
概念说明
文件松弛空间文件末尾到簇边界之间的未使用空间,包含残留数据
RAM松弛空间松弛空间中从文件末尾到扇区边界的部分(历史上填充RAM内容或零值)
MFT ($MFT)主文件表 - NTFS的元数据数据库,包含每个文件的条目
USN日志 ($UsnJrnl)变更日志,记录NTFS上所有文件/目录的修改操作
备用数据流NTFS的特性,允许每个文件拥有多个数据流(用于隐藏存储)
$STANDARD_INFORMATIONMFT属性,包含可由用户模式应用修改的时间戳
$FILE_NAMEMFT属性,包含仅可由内核修改的时间戳
时间戳篡改一种反取证技术,修改文件时间戳以避免被检测

Tools & Systems

工具与系统

ToolPurpose
MFTECmdEric Zimmerman MFT and USN Journal parser with CSV output
MFTExplorerInteractive GUI tool for MFT analysis
analyzeMFTPython MFT parser with CSV/JSON output
The Sleuth KitFile system forensics toolkit (fls, icat, blkls, istat)
bulk_extractorFeature extraction from raw data including slack space
NTFS Log TrackerTool for parsing $LogFile transaction records
streams.exeSysinternals tool for listing NTFS Alternate Data Streams
PlasoSuper-timeline tool parsing MFT and USN Journal
工具用途
MFTECmdEric Zimmerman开发的MFT和USN日志解析工具,支持CSV输出
MFTExplorer用于MFT分析的交互式GUI工具
analyzeMFT支持CSV/JSON输出的Python MFT解析器
The Sleuth Kit文件系统取证工具包(包含fls、icat、blkls、istat)
bulk_extractor从原始数据(包括松弛空间)中提取特征的工具
NTFS Log Tracker用于解析$LogFile事务记录的工具
streams.exeSysinternals出品的NTFS备用数据流列表工具
Plaso解析MFT和USN日志的超级时间线工具

Common Scenarios

常见场景

Scenario 1: Anti-Forensics Detection via Timestomping Compare $STANDARD_INFORMATION timestamps with $FILE_NAME timestamps in MFT entries, flag files where $SI timestamps predate $FN timestamps (impossible in normal operation), identify timestomped files as evidence of deliberate manipulation, correlate with other timeline evidence.
Scenario 2: Hidden Data in Alternate Data Streams Scan for ADS attached to files beyond the standard Zone.Identifier, extract ADS content for analysis, check for hidden executables or documents stored in ADS, correlate ADS creation with user activity timeline, document findings for evidence.
Scenario 3: Deleted File Reconstruction from MFT Parse MFT for inactive (deleted) entries, extract filenames, sizes, and timestamps of deleted files, recover file content using icat if data clusters are not overwritten, build list of deleted evidence files, correlate with USN Journal delete events.
Scenario 4: File Activity Reconstruction from USN Journal Parse the USN Change Journal for the investigation period, identify file creation, modification, rename, and deletion events, reconstruct the sequence of file operations, detect evidence of data staging (create, copy, compress, delete pattern), identify anti-forensic file wiping.
场景1:通过时间戳篡改检测反取证行为 比较MFT条目中的$STANDARD_INFORMATION时间戳与$FILE_NAME时间戳,标记$SI时间戳早于$FN时间戳的文件(正常操作中不可能出现),将这些时间戳篡改的文件识别为故意操作的证据,并与其他时间线证据关联。
场景2:备用数据流中的隐藏数据 扫描标准Zone.Identifier之外的附加ADS,提取ADS内容进行分析,检查存储在ADS中的隐藏可执行文件或文档,将ADS创建时间与用户活动时间线关联,记录调查结果作为证据。
场景3:从MFT重建已删除文件 解析MFT中的无效(已删除)条目,提取已删除文件的文件名、大小和时间戳,如果数据簇未被覆盖,使用icat恢复文件内容,构建已删除证据文件列表,并与USN日志中的删除事件关联。
场景4:从USN日志重建文件活动 解析调查期间的USN变更日志,识别文件创建、修改、重命名和删除事件,重建文件操作序列,检测数据暂存模式(创建、复制、压缩、删除的模式),识别反取证的文件擦除行为。

Output Format

输出格式

File System Artifact Analysis:
  Volume: NTFS (Partition 2, 465 GB)
  Cluster Size: 4096 bytes

  MFT Analysis:
    Total Entries: 456,789
    Active Files: 234,567
    Deleted Entries: 12,345 (8,901 with recoverable metadata)
    Timestomped Files: 23 (SI/FN mismatch detected)

  USN Journal:
    Records Parsed: 2,345,678
    Date Range: 2024-01-01 to 2024-01-20
    File Creations: 45,678
    File Deletions: 23,456
    File Renames: 12,345

  Alternate Data Streams:
    Total ADS Found: 1,234
    Zone.Identifier: 890 (downloaded files)
    Custom/Suspicious ADS: 5 (hidden data detected)

  Slack Space:
    Total Slack: 12.3 GB
    Keyword Hits: 45 (passwords, credit cards)
    Carved Files: 23 from slack space

  Suspicious Findings:
    - 23 files with timestomped timestamps
    - 5 files with hidden ADS containing data
    - USN shows mass deletion on 2024-01-18 (anti-forensics)
    - Slack space contains residual email fragments

  Reports: /cases/case-2024-001/analysis/
File System Artifact Analysis:
  Volume: NTFS (Partition 2, 465 GB)
  Cluster Size: 4096 bytes

  MFT Analysis:
    Total Entries: 456,789
    Active Files: 234,567
    Deleted Entries: 12,345 (8,901 with recoverable metadata)
    Timestomped Files: 23 (SI/FN mismatch detected)

  USN Journal:
    Records Parsed: 2,345,678
    Date Range: 2024-01-01 to 2024-01-20
    File Creations: 45,678
    File Deletions: 23,456
    File Renames: 12,345

  Alternate Data Streams:
    Total ADS Found: 1,234
    Zone.Identifier: 890 (downloaded files)
    Custom/Suspicious ADS: 5 (hidden data detected)

  Slack Space:
    Total Slack: 12.3 GB
    Keyword Hits: 45 (passwords, credit cards)
    Carved Files: 23 from slack space

  Suspicious Findings:
    - 23 files with timestomped timestamps
    - 5 files with hidden ADS containing data
    - USN shows mass deletion on 2024-01-18 (anti-forensics)
    - Slack space contains residual email fragments

  Reports: /cases/case-2024-001/analysis/