--- name: analyzing-slack-space-and-file-system-artifacts-06ec8630ca23 description: Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available file-system change activity from USN records. Use during deep forensic analysis of an NTFS image when standard file recovery is insufficient, such as hunting for data hidden in ADS. domain: cybersecurity subdomain: digital-forensics tags: - forensics - slack-space - ntfs - mft - usn-journal - alternate-data-streams - file-system-analysis version: '1.0' author: mahipal license: Apache-2.0 nist_csf: - RS.AN-03 - DE.AE-02 - RS.MA-01 mitre_attack: - T1070.006 - T1564.004 - T1070.004 - T1005 - T1006 --- # Analyzing Slack Space and File System Artifacts ## When to Use - 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 ## 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 ```bash # 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) ```bash # 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 ```bash # 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 ```bash # 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(' 65536 or offset + record_len > len(data): return None, offset + 8 major_ver = struct.unpack_from('/dev/null | grep -i "ads\|zone\|stream" # 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 # Extract specific ADS content # 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 # 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 # Zone.Identifier content reveals: # [ZoneTransfer] # ZoneId=3 (3 = Internet, indicating file was downloaded) # ReferrerUrl=https://malicious-site.com/payload.exe # HostUrl=https://cdn.malicious-site.com/payload.exe ``` ## Key Concepts | Concept | Description | |---------|-------------| | File slack | 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/ ``` --- Source attribution: hironow Original project: https://github.com/hironow/cyber/blob/20efb3a6a2a9e7ebbde38ed32325979866110540/.agents/skills/analyzing-slack-space-and-file-system-artifacts/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: hironow Original project: https://github.com/hironow/cyber/blob/20efb3a6a2a9e7ebbde38ed32325979866110540/.agents/skills/analyzing-slack-space-and-file-system-artifacts/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Data Analyst, ML / AI Engineer, Security Engineer Specialties: Specialty not specified Tools: Mftecmd, Mftexplorer, Analyzemft, Sleuth Kit Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 47f4b362364a03828d9321fd47af921a0093238c1f5b88d2a4e6486210cb4714