Disk Forensics

Comprehensive disk forensics skill for analyzing storage media, file systems, and persistent artifacts. Enables recovery of deleted files, analysis of file system metadata, detection of hidden data, and extraction of forensic artifacts from disk images.

Capabilities

• Disk Image Acquisition: Create forensically sound disk images with integrity verification
• File System Analysis: Parse and analyze NTFS, FAT, EXT, HFS+, APFS file systems
• Deleted File Recovery: Recover deleted files using file carving and file system analysis
• MFT Analysis: Parse NTFS Master File Table for file metadata and timestamps
• Slack Space Analysis: Examine slack space for hidden or residual data
• Alternate Data Streams: Detect and extract NTFS alternate data streams
• File Signature Analysis: Verify file signatures and detect mismatched extensions
• Hash Analysis: Calculate and verify file hashes for integrity and known file detection
• Volume Shadow Copy Analysis: Extract and analyze Windows Volume Shadow Copies
• Partition Analysis: Detect hidden partitions, analyze partition tables

Quick Start

from disk_forensics import DiskAnalyzer, FileRecovery, MFTParser

# Initialize analyzer with disk image
analyzer = DiskAnalyzer("/evidence/disk_image.E01")

# Get volume information
volumes = analyzer.list_volumes()
for vol in volumes:
    print(f"Volume: {vol.description} - {vol.size_gb}GB")

# Recover deleted files
recovery = FileRecovery(analyzer)
deleted = recovery.find_deleted_files()

# Parse MFT
mft_parser = MFTParser(analyzer)
entries = mft_parser.parse_all()

Usage

Task 1: Disk Image Acquisition

Input: Physical disk or logical volume to acquire

Process:

1. Document source media details
2. Calculate source hash before acquisition
3. Create forensic image (E01/Ex01/raw)
4. Verify image integrity with hash comparison
5. Generate acquisition report

Output: Forensically sound disk image with documentation

Example:

from disk_forensics import DiskAcquisition

# Initialize acquisition
acquisition = DiskAcquisition()

# Document source
source_info = acquisition.document_source(
    device_path="/dev/sdb",
    make="Samsung",
    model="SSD 870 EVO",
    serial_number="S5XXXXXXXXXXXX",
    capacity_gb=500
)

# Create forensic image
result = acquisition.create_image(
    source="/dev/sdb",
    destination="/evidence/suspect_disk.E01",
    format="ewf",  # Expert Witness Format
    compression="best",
    segment_size_gb=2,
    hash_algorithms=["md5", "sha256"]
)

print(f"Acquisition complete")
print(f"Source Hash: {result.source_hash}")
print(f"Image Hash: {result.image_hash}")
print(f"Verified: {result.verified}")

# Generate acquisition report
acquisition.generate_report(
    output_path="/evidence/acquisition_report.pdf",
    case_id="CASE-2024-001",
    examiner="Jane Smith"
)

Task 2: File System Analysis

Input: Disk image file path

Process:

1. Mount disk image read-only
2. Identify file system type
3. Parse file system structures
4. Extract file metadata
5. Build file system timeline

Output: File system analysis with metadata

Example:

from disk_forensics import DiskAnalyzer, FileSystemParser

analyzer = DiskAnalyzer("/evidence/disk_image.E01")

# List all volumes
volumes = analyzer.list_volumes()
for vol in volumes:
    print(f"Volume {vol.index}: {vol.file_system}")
    print(f"  Start: {vol.start_offset}")
    print(f"  Size: {vol.size_bytes} bytes")

# Parse specific volume
parser = FileSystemParser(analyzer, volume_index=2)

# Get volume statistics
stats = parser.get_statistics()
print(f"Total files: {stats.total_files}")
print(f"Total directories: {stats.total_directories}")
print(f"Deleted entries: {stats.deleted_entries}")

# List directory contents
files = parser.list_directory("/Users/suspect/Documents")
for f in files:
    print(f"{f.name} - {f.size} bytes - {f.modified_time}")

# Find files by extension
docs = parser.find_files_by_extension([".docx", ".xlsx", ".pdf"])

# Find files by date range
recent = parser.find_files_by_date(
    start_date="2024-01-01",
    end_date="2024-01-31",
    date_type="modified"
)

Task 3: Deleted File Recovery

Input: Disk image with potential deleted files

Process:

1. Scan file system for deleted entries
2. Analyze unallocated space
3. Perform file carving by signatures
4. Verify recovered file integrity
5. Document recovery results

Output: Recovered files with recovery metadata

Example:

from disk_forensics import DiskAnalyzer, FileRecovery

analyzer = DiskAnalyzer("/evidence/disk_image.E01")
recovery = FileRecovery(analyzer)

# Find deleted files via file system
deleted = recovery.find_deleted_files()
for f in deleted:
    print(f"Deleted: {f.name}")
    print(f"  Original path: {f.original_path}")
    print(f"  Size: {f.size}")
    print(f"  Recoverable: {f.recoverable_percent}%")

# Recover specific file
recovery.recover_file(
    file_entry=deleted[0],
    output_path="/evidence/recovered/"
)

# File carving from unallocated space
carved = recovery.carve_files(
    file_types=["jpg", "png", "pdf", "docx"],
    output_dir="/evidence/carved/"
)

for f in carved:
    print(f"Carved: {f.filename}")
    print(f"  Type: {f.file_type}")
    print(f"  Size: {f.size}")
    print(f"  Offset: {f.disk_offset}")

# Recovery statistics
stats = recovery.get_statistics()
print(f"Files recovered: {stats.files_recovered}")
print(f"Data recovered: {stats.bytes_recovered} bytes")

Task 4: MFT Analysis (NTFS)

Input: NTFS disk image or extracted MFT file

Process:

1. Locate and extract MFT
2. Parse MFT entries
3. Extract standard information attributes
4. Analyze file names and timestamps
5. Detect timestamp manipulation

Output: MFT analysis with timeline anomalies

Example:

from disk_forensics import DiskAnalyzer, MFTParser

analyzer = DiskAnalyzer("/evidence/disk_image.E01")
mft_parser = MFTParser(analyzer, volume_index=2)

# Parse entire MFT
entries = mft_parser.parse_all()
print(f"Total MFT entries: {len(entries)}")

# Get specific file entry
entry = mft_parser.get_entry_by_path("/Users/suspect/malware.exe")
if entry:
    print(f"File: {entry.filename}")
    print(f"Created: {entry.created_time}")
    print(f"Modified: {entry.modified_time}")
    print(f"Accessed: {entry.accessed_time}")
    print(f"MFT Modified: {entry.mft_modified_time}")

# Detect timestamp anomalies (timestomping)
anomalies = mft_parser.detect_timestamp_anomalies()
for a in anomalies:
    print(f"ANOMALY: {a.filename}")
    print(f"  Type: {a.anomaly_type}")
    print(f"  Details: {a.description}")

# Find files by MFT entry number
entry = mft_parser.get_entry_by_number(12345)

# Extract MFT to file
mft_parser.extract_mft("/evidence/extracted_mft.bin")

# Generate MFT timeline
mft_parser.export_timeline("/evidence/mft_timeline.csv")

Task 5: Alternate Data Streams Analysis

Input: NTFS disk image

Process:

1. Scan for files with alternate data streams
2. Extract ADS content
3. Analyze ADS for malicious content
4. Check Zone.Identifier streams
5. Document ADS findings

Output: ADS inventory with extracted content

Example:

from disk_forensics import DiskAnalyzer, ADSScanner

analyzer = DiskAnalyzer("/evidence/disk_image.E01")
ads_scanner = ADSScanner(analyzer, volume_index=2)

# Find all alternate data streams
streams = ads_scanner.find_all_streams()

for stream in streams:
    print(f"File: {stream.parent_file}")
    print(f"  Stream: {stream.stream_name}")
    print(f"  Size: {stream.size} bytes")

# Extract specific stream
ads_scanner.extract_stream(
    file_path="/Users/suspect/document.docx",
    stream_name="Zone.Identifier",
    output_path="/evidence/zone_id.txt"
)

# Analyze Zone.Identifier streams (download origins)
zone_info = ads_scanner.analyze_zone_identifiers()
for zi in zone_info:
    print(f"File: {zi.filename}")
    print(f"  Download URL: {zi.referrer_url}")
    print(f"  Host URL: {zi.host_url}")
    print(f"  Zone: {zi.security_zone}")

# Find executable content in ADS
suspicious = ads_scanner.find_executable_ads()
for s in suspicious:
    print(f"SUSPICIOUS: {s.parent_file}:{s.stream_name}")

Task 6: Volume Shadow Copy Analysis

Input: Windows disk image with VSS

Process:

1. Enumerate Volume Shadow Copies
2. Mount shadow copy for analysis
3. Compare files across shadow copies
4. Extract previous file versions
5. Timeline shadow copy changes

Output: VSS analysis with file version history

Example:

from disk_forensics import DiskAnalyzer, VSSAnalyzer

analyzer = DiskAnalyzer("/evidence/disk_image.E01")
vss_analyzer = VSSAnalyzer(analyzer, volume_index=2)

# List all shadow copies
shadows = vss_analyzer.list_shadow_copies()
for sc in shadows:
    print(f"Shadow Copy: {sc.id}")
    print(f"  Created: {sc.creation_time}")
    print(f"  Volume: {sc.volume_path}")

# Get file from specific shadow copy
file_content = vss_analyzer.extract_file(
    shadow_id=shadows[0].id,
    file_path="/Users/suspect/deleted_evidence.xlsx",
    output_path="/evidence/recovered_from_vss.xlsx"
)

# Compare file across shadow copies
diff = vss_analyzer.compare_file_versions(
    file_path="/Users/suspect/important.docx"
)
for version in diff:
    print(f"Version from {version.shadow_date}:")
    print(f"  Size: {version.size}")
    print(f"  Hash: {version.hash}")

# Find deleted files recoverable from VSS
recoverable = vss_analyzer.find_deleted_in_shadows()

# Export VSS timeline
vss_analyzer.export_timeline("/evidence/vss_timeline.csv")

Task 7: File Signature Analysis

Input: Disk image or directory of files

Process:

1. Extract file headers/signatures
2. Compare to known file signatures
3. Identify mismatched extensions
4. Detect embedded files
5. Report signature anomalies

Output: File signature analysis with mismatches

Example:

from disk_forensics import DiskAnalyzer, SignatureAnalyzer

analyzer = DiskAnalyzer("/evidence/disk_image.E01")
sig_analyzer = SignatureAnalyzer(analyzer, volume_index=2)

# Analyze all files
results = sig_analyzer.analyze_all()

# Find extension mismatches
mismatches = sig_analyzer.find_mismatches()
for m in mismatches:
    print(f"MISMATCH: {m.file_path}")
    print(f"  Extension: {m.extension}")
    print(f"  Actual Type: {m.detected_type}")
    print(f"  Signature: {m.signature_hex}")

# Analyze specific file
file_info = sig_analyzer.analyze_file("/Users/suspect/image.jpg")
print(f"File: {file_info.path}")
print(f"Detected Type: {file_info.detected_type}")
print(f"MIME Type: {file_info.mime_type}")
print(f"Extension Valid: {file_info.extension_valid}")

# Find renamed executables
renamed_exe = sig_analyzer.find_renamed_executables()
for exe in renamed_exe:
    print(f"Hidden EXE: {exe.path} (disguised as {exe.extension})")

# Detect polyglot files (multiple valid signatures)
polyglots = sig_analyzer.find_polyglots()

# Export analysis report
sig_analyzer.export_report("/evidence/signature_analysis.csv")

Task 8: Slack Space Analysis

Input: Disk image file

Process:

1. Identify file slack space locations
2. Extract slack space content
3. Search for readable data
4. Identify potential evidence
5. Document findings

Output: Slack space analysis with extracted data

Example:

from disk_forensics import DiskAnalyzer, SlackSpaceAnalyzer

analyzer = DiskAnalyzer("/evidence/disk_image.E01")
slack_analyzer = SlackSpaceAnalyzer(analyzer, volume_index=2)

# Analyze all slack space
results = slack_analyzer.analyze_all()
print(f"Total slack space: {results.total_bytes} bytes")
print(f"Slack with data: {results.data_bytes} bytes")

# Extract slack space from specific file
slack_data = slack_analyzer.get_file_slack("/Users/suspect/document.docx")
print(f"Slack content: {slack_data.content[:100]}")

# Search slack space for patterns
matches = slack_analyzer.search_slack(
    patterns=["password", "secret", "confidential"],
    case_sensitive=False
)
for m in matches:
    print(f"Found '{m.pattern}' in slack of {m.file_path}")
    print(f"  Context: {m.context}")

# Extract all readable strings from slack
strings = slack_analyzer.extract_strings(min_length=4)

# Export slack space content
slack_analyzer.export_slack_data("/evidence/slack_space/")

Task 9: Partition Analysis

Input: Raw disk image or physical device

Process:

1. Read partition table (MBR/GPT)
2. Identify all partitions
3. Detect hidden partitions
4. Analyze unallocated space
5. Document partition layout

Output: Complete partition analysis

Example:

from disk_forensics import DiskAnalyzer, PartitionAnalyzer

analyzer = DiskAnalyzer("/evidence/full_disk.dd")
partition_analyzer = PartitionAnalyzer(analyzer)

# Get partition table type
pt_type = partition_analyzer.get_partition_table_type()
print(f"Partition Table: {pt_type}")

# List all partitions
partitions = partition_analyzer.list_partitions()
for p in partitions:
    print(f"Partition {p.index}:")
    print(f"  Type: {p.type_name}")
    print(f"  Start: {p.start_sector}")
    print(f"  Size: {p.size_bytes} bytes")
    print(f"  File System: {p.file_system}")
    print(f"  Bootable: {p.bootable}")

# Detect hidden partitions
hidden = partition_analyzer.find_hidden_partitions()
for h in hidden:
    print(f"HIDDEN: Found at sector {h.start_sector}")

# Analyze gaps between partitions
gaps = partition_analyzer.find_unallocated_space()
for gap in gaps:
    print(f"Unallocated: {gap.start_sector} - {gap.end_sector}")
    print(f"  Size: {gap.size_bytes} bytes")

# Analyze deleted partitions
deleted = partition_analyzer.find_deleted_partitions()

# Export partition map
partition_analyzer.export_map("/evidence/partition_map.json")

Task 10: Hash Analysis and Known File Detection

Input: Disk image or file collection

Process:

1. Calculate hashes for all files
2. Compare against known file databases
3. Identify known good files (NSRL)
4. Flag known malicious files
5. Generate hash report

Output: Hash analysis with categorization

Example:

from disk_forensics import DiskAnalyzer, HashAnalyzer

analyzer = DiskAnalyzer("/evidence/disk_image.E01")
hash_analyzer = HashAnalyzer(analyzer, volume_index=2)

# Calculate hashes for all files
hashes = hash_analyzer.hash_all_files(
    algorithms=["md5", "sha1", "sha256"]
)

# Compare against NSRL (known good files)
nsrl_results = hash_analyzer.check_nsrl(
    nsrl_path="/hashsets/NSRLFile.txt"
)
print(f"Known good files: {nsrl_results.known_count}")
print(f"Unknown files: {nsrl_results.unknown_count}")

# Check against malware hash database
malware_check = hash_analyzer.check_malware_hashes(
    hash_db="/hashsets/malware_hashes.txt"
)
for match in malware_check.matches:
    print(f"MALWARE: {match.file_path}")
    print(f"  Hash: {match.hash}")
    print(f"  Malware Name: {match.malware_name}")

# Find duplicate files
duplicates = hash_analyzer.find_duplicates()
for dup_group in duplicates:
    print(f"Duplicate files (hash: {dup_group.hash}):")
    for f in dup_group.files:
        print(f"  - {f}")

# Export hash report
hash_analyzer.export_report(
    output_path="/evidence/hash_report.csv",
    format="csv"
)

Configuration

Environment Variables

SLEUTHKIT_PATH

NSRL_PATH

YARA_RULES

CARVING_SIGNATURES

Options

verify_image

cache_metadata

parallel_hash

carving_depth

timezone

Examples

Example 1: Data Theft Investigation

Scenario: Investigating potential intellectual property theft

from disk_forensics import DiskAnalyzer, FileSystemParser, MFTParser

# Load suspect's disk image
analyzer = DiskAnalyzer("/evidence/suspect_laptop.E01")
parser = FileSystemParser(analyzer, volume_index=2)

# Find recently accessed sensitive documents
recent_docs = parser.find_files_by_date(
    start_date="2024-01-01",
    end_date="2024-01-31",
    date_type="accessed",
    extensions=[".docx", ".xlsx", ".pdf", ".pptx"]
)

# Check USB device history
usb_artifacts = analyzer.get_usb_history()
for device in usb_artifacts:
    print(f"USB: {device.device_name}")
    print(f"  First connected: {device.first_connected}")
    print(f"  Last connected: {device.last_connected}")

# Analyze MFT for deleted documents
mft = MFTParser(analyzer, volume_index=2)
deleted = mft.find_deleted_entries(extensions=[".docx", ".xlsx"])

# Check cloud sync folders
cloud_folders = [
    "/Users/suspect/Dropbox",
    "/Users/suspect/OneDrive",
    "/Users/suspect/Google Drive"
]
for folder in cloud_folders:
    files = parser.list_directory(folder, recursive=True)
    print(f"Found {len(files)} files in {folder}")

Example 2: Malware Persistence Analysis

Scenario: Finding malware persistence mechanisms on disk

from disk_forensics import DiskAnalyzer, FileSystemParser, SignatureAnalyzer

analyzer = DiskAnalyzer("/evidence/infected_system.E01")
parser = FileSystemParser(analyzer, volume_index=2)
sig_analyzer = SignatureAnalyzer(analyzer, volume_index=2)

# Check common persistence locations
persistence_paths = [
    "/Windows/System32/Tasks",
    "/Users/*/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/Startup",
    "/ProgramData/Microsoft/Windows/Start Menu/Programs/Startup"
]

for path in persistence_paths:
    files = parser.list_directory(path)
    for f in files:
        print(f"Persistence: {f.name} - Created: {f.created_time}")

# Find hidden executables
hidden_exe = sig_analyzer.find_renamed_executables()

# Analyze Windows prefetch
prefetch_files = parser.find_files_by_extension([".pf"],
    path="/Windows/Prefetch")

# Check for suspicious services
services = parser.get_file("/Windows/System32/config/SYSTEM")

Example 3: Deleted File Recovery Operation

Scenario: Recovering deleted evidence

from disk_forensics import DiskAnalyzer, FileRecovery, VSSAnalyzer

analyzer = DiskAnalyzer("/evidence/suspect_disk.E01")

# Method 1: File system recovery
recovery = FileRecovery(analyzer)
fs_deleted = recovery.find_deleted_files()
print(f"Found {len(fs_deleted)} deleted files in file system")

# Method 2: File carving
carved = recovery.carve_files(
    file_types=["jpg", "png", "pdf", "docx", "xlsx"],
    output_dir="/evidence/carved_files/"
)
print(f"Carved {len(carved)} files from unallocated space")

# Method 3: Volume Shadow Copy recovery
vss = VSSAnalyzer(analyzer, volume_index=2)
shadows = vss.list_shadow_copies()

for shadow in shadows:
    vss_files = vss.list_deleted_in_shadow(shadow.id)
    for f in vss_files:
        vss.extract_file(shadow.id, f.path,
            f"/evidence/vss_recovery/{shadow.id}/{f.name}")

Limitations

• Maximum supported disk image size depends on system resources
• EWF compression may slow analysis on large images
• File carving cannot recover fragmented files completely
• Encrypted volumes require decryption keys
• Some file systems may have limited support
• VSS analysis requires Windows images
• Hash database comparison requires external databases

Troubleshooting

Common Issue 1: Image Mount Failure

Problem: Unable to mount or read disk image Solution:

• Verify image integrity with hash verification
• Check for supported image format (raw, E01, AFF)
• Ensure adequate disk space for cache

Common Issue 2: File System Not Recognized

Problem: Unknown file system type Solution:

• Check partition offset alignment
• Try manual file system specification
• Verify image is not encrypted

Common Issue 3: Carving Produces Corrupt Files

Problem: Carved files are damaged or incomplete Solution:

• Files may be fragmented
• Increase carving validation settings
• Use multiple carving tools for verification

Common Issue 4: Slow Hash Calculation

Problem: Hashing takes too long Solution:

• Enable parallel processing
• Use faster hash algorithm (MD5 vs SHA-256)
• Exclude known good files

Related Skills

• memory-forensics: Volatile memory analysis
• timeline-forensics: Super timeline creation
• artifact-collection: Evidence collection procedures
• registry-forensics: Windows registry analysis
• malware-forensics: Malware sample analysis

References

• Disk Forensics Reference
• File System Analysis Guide
• File Carving Signatures