Examine file system slack space, MFT entries, USN journal, and alternate data streams to recover hidden data and reconstruct file activity on NTFS volumes.
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Examine file system slack space, MFT entries, USN journal, and alternate data streams to recover hidden data and reconstruct file activity on NTFS volumes.
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
Detection Gaps & Validation
Most-missed artifacts: beyond file slack, examine MFT record slack (prior-file remnants in the 1024-byte entry), resident $DATA of small deleted files, $LogFile transactions (which survive after the USN wraps), $Bitmap to test cluster reallocation, and non-Zone.Identifier ADS. fls/Explorer hide ADS - enumerate them explicitly and carve unallocated with bulk_extractor/foremost.
USN wrap and slack overwrite:$UsnJrnl:$J is sparse and circular, so older events vanish on wrap - absence is not proof. Slack/unallocated is volatile too: any new write can overwrite the residual data, so a clean slack region does not mean nothing was hidden. Corroborate with $LogFile and Volume Shadow Copies.
Timestomping detection ($SI vs $FN):$STANDARD_INFORMATION times are user-settable; $FILE_NAME times are kernel-only. Flag $SI predating $FN, identical/zeroed sub-second values, or $SI inconsistent with MFT entry-number ordering, then confirm against $LogFile/USN before calling it deliberate.
Validate with a second source: corroborate a recovered or hidden artifact across at least two of - MFT, USN, $LogFile, Volume Shadow Copies, Prefetch, Recycle Bin $I - rather than a single slack-string hit; a keyword in slack proves the bytes existed, not who wrote them or when.
Interpretation pitfalls (false positives): installers/copies legitimately carry old $SI dates and NTFS tunneling re-applies them on recreate; benign apps (browsers, AV) create ADS; defrag/TRIM on SSDs wipes slack and unallocated, destroying evidence. Confirm volume timezone and clock skew before timelining.
Prerequisites
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
Workflow
Step 1: Identify and Extract NTFS File System Artifacts
# Determine partition layout
mmls /cases/case-2024-001/images/evidence.dd
# Extract key NTFS system files# $MFT - Master File Table
icat -o 2048 /cases/case-2024-001/images/evidence.dd 0 > /cases/case-2024-001/ntfs/MFT
# $UsnJrnl:$J - USN Change Journal
icat -o 2048 /cases/case-2024-001/images/evidence.dd 62-128 > /cases/case-2024-001/ntfs/UsnJrnl_J
# $LogFile - Transaction log
icat -o 2048 /cases/case-2024-001/images/evidence.dd 2 > /cases/case-2024-001/ntfs/LogFile
# 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
# Get file system information
fsstat -o 2048 /cases/case-2024-001/images/evidence.dd | tee /cases/case-2024-001/ntfs/fs_info.txt
Step 2: Analyze the Master File Table (MFT)
# Parse MFT with MFTECmd (Eric Zimmerman)
MFTECmd.exe -f "C:\cases\ntfs\MFT" --csv "C:\cases\analysis\" --csvf mft_analysis.csv
# 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
# 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
Step 3: Analyze Slack Space for Hidden Data
# Search slack space for strings
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
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
python3 << 'PYEOF'
import struct
# File slack consists of:# 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# Analyze slack for specific MFT entries# Using Sleuth Kit to get file slack for a specific file
import subprocess
# 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)
# The output shows data runs - the last cluster may contain slack data# Calculate slack size: (allocated_size - file_size) bytes
PYEOF
# 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/
# 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
Step 4: Parse the USN Change Journal
# Parse USN Journal with MFTECmd
MFTECmd.exe -f "C:\cases\ntfs\UsnJrnl_J" --csv "C:\cases\analysis\" --csvf usn_journal.csv
# 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
# 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
# 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']})")
# 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
Unused space between file end and cluster boundary containing residual data
RAM slack
Portion 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 Streams
NTFS feature allowing multiple data streams per file (hidden storage)
$STANDARD_INFORMATION
MFT attribute with timestamps modifiable by user-mode applications
$FILE_NAME
MFT attribute with timestamps only modifiable by the kernel
Timestomping
Anti-forensic technique modifying file timestamps to avoid detection
Tools & Systems
Tool
Purpose
MFTECmd
Eric Zimmerman MFT and USN Journal parser with CSV output
MFTExplorer
Interactive GUI tool for MFT analysis
analyzeMFT
Python MFT parser with CSV/JSON output
The Sleuth Kit
File system forensics toolkit (fls, icat, blkls, istat)
bulk_extractor
Feature extraction from raw data including slack space
NTFS Log Tracker
Tool for parsing $LogFile transaction records
streams.exe
Sysinternals tool for listing NTFS Alternate Data Streams
Plaso
Super-timeline tool parsing MFT and USN Journal
Common Scenarios
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.
Output Format
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/
