Analyzing Disk Image with Autopsy
When to Use
- When you have a forensic disk image and need structured analysis of its contents
- During investigations requiring file recovery, keyword searching, and timeline analysis
- When non-technical stakeholders need visual reports from forensic evidence
- For examining file system metadata, deleted files, and embedded artifacts
- When building a comprehensive case from multiple disk images
Prerequisites
- Autopsy 4.x installed (Windows) or Autopsy 4.x with The Sleuth Kit (Linux)
- Forensic disk image in raw (dd), E01 (EnCase), or AFF format
- Minimum 8GB RAM (16GB recommended for large images)
- Java Runtime Environment (JRE) 8+ for Autopsy
- Sufficient disk space for the Autopsy case database (2-3x image size)
- Hash databases (NSRL, known-bad hashes) for file identification
Workflow
Step 1: Install Autopsy and Configure Environment
bash# On Linux, install Sleuth Kit and Autopsy sudo apt-get install autopsy sleuthkit # Download Autopsy 4.x (GUI version) from official source wget https://github.com/sleuthkit/autopsy/releases/download/autopsy-4.21.0/autopsy-4.21.0.zip unzip autopsy-4.21.0.zip -d /opt/autopsy # On Windows, run the MSI installer from sleuthkit.org # Launch Autopsy /opt/autopsy/bin/autopsy --nosplash # For Sleuth Kit command-line analysis alongside Autopsy sudo apt-get install sleuthkit
Step 2: Create a New Case and Add the Disk Image
1. Launch Autopsy > "New Case" 2. Enter Case Name: "CASE-2024-001-Workstation" 3. Set Base Directory: /cases/case-2024-001/autopsy/ 4. Enter Case Number, Examiner Name 5. Click "Add Data Source" 6. Select "Disk Image or VM File" 7. Browse to: /cases/case-2024-001/images/evidence.dd 8. Select Time Zone of the original system 9. Configure Ingest Modules (see Step 3)
bash# Alternatively, use Sleuth Kit CLI to verify the image first img_stat /cases/case-2024-001/images/evidence.dd # List partitions in the image mmls /cases/case-2024-001/images/evidence.dd # Output example: # DOS Partition Table # Offset Sector: 0 # Units are in 512-byte sectors # Slot Start End Length Description # 00: ----- 0000000000 0000002047 0000002048 Primary Table (#0) # 01: 00:00 0000002048 0001026047 0001024000 NTFS (0x07) # 02: 00:01 0001026048 0976771071 0975745024 NTFS (0x07) # List files in a partition (offset 2048 sectors) fls -o 2048 /cases/case-2024-001/images/evidence.dd
Step 3: Configure and Run Ingest Modules
Enable the following Autopsy Ingest Modules: - Recent Activity: Extracts browser history, downloads, cookies, bookmarks - Hash Lookup: Compares files against NSRL and known-bad hash sets - File Type Identification: Identifies files by signature, not extension - Keyword Search: Indexes content for full-text searching - Email Parser: Extracts emails from PST, MBOX, EML files - Extension Mismatch Detector: Finds files with wrong extensions - Exif Parser: Extracts metadata from images (GPS, camera, timestamps) - Encryption Detection: Identifies encrypted files and containers - Interesting Files Identifier: Flags files matching custom rule sets - Embedded File Extractor: Extracts files from ZIP, Office docs, PDFs - Picture Analyzer: Categorizes images using PhotoDNA or hash matching - Data Source Integrity: Verifies image hash during ingest
bash# Configure NSRL hash set for known-good filtering # Download NSRL from https://www.nist.gov/itl/ssd/software-quality-group/national-software-reference-library-nsrl wget https://s3.amazonaws.com/rds.nsrl.nist.gov/RDS/current/rds_modernm.zip unzip rds_modernm.zip -d /opt/autopsy/hashsets/ # Import into Autopsy: # Tools > Options > Hash Sets > Import > Select NSRLFile.txt # Mark as "Known" (to filter out known-good files)
Step 4: Analyze File System and Recover Deleted Files
bash# In Autopsy GUI: Navigate tree structure # - Data Sources > evidence.dd > vol2 (NTFS) # - Examine directory tree, note deleted files (marked with X) # Using Sleuth Kit CLI for targeted recovery # List deleted files fls -rd -o 2048 /cases/case-2024-001/images/evidence.dd # Recover a specific deleted file by inode icat -o 2048 /cases/case-2024-001/images/evidence.dd 14523 > /cases/case-2024-001/recovered/deleted_document.docx # Extract all files from a directory tsk_recover -o 2048 -d /Users/suspect/Documents \ /cases/case-2024-001/images/evidence.dd \ /cases/case-2024-001/recovered/documents/ # Get detailed file metadata istat -o 2048 /cases/case-2024-001/images/evidence.dd 14523 # Shows: creation, modification, access, MFT change timestamps, size, data runs
Step 5: Perform Keyword Searches and Tag Evidence
In Autopsy: 1. Keyword Search panel > "Ad Hoc Keyword Search" 2. Search terms: credit card patterns, SSN regex, email addresses 3. Example regex for credit cards: \b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14})\b 4. Example regex for SSN: \b\d{3}-\d{2}-\d{4}\b 5. Review results > Right-click items > "Add Tag" 6. Create tags: "Evidence-Critical", "Evidence-Supporting", "Requires-Review" 7. Add comments to tagged items documenting relevance
bash# Using Sleuth Kit for CLI keyword search srch_strings -a -o 2048 /cases/case-2024-001/images/evidence.dd | \ grep -iE '(password|secret|confidential)' > /cases/case-2024-001/keyword_hits.txt # Search for specific file signatures sigfind -o 2048 /cases/case-2024-001/images/evidence.dd 25504446 # 25504446 = %PDF header signature
Step 6: Build Timeline and Generate Reports
In Autopsy: 1. Timeline viewer: Tools > Timeline 2. Select date range of interest (incident window) 3. Filter by event type: File Created, Modified, Accessed, Web Activity 4. Zoom into suspicious time periods 5. Export timeline events as CSV for external analysis Generate Report: 1. Generate Report > HTML Report 2. Select tagged items and data sources to include 3. Configure report sections: file listings, keyword hits, timeline 4. Export to /cases/case-2024-001/reports/
bash# Using Sleuth Kit mactime for CLI timeline fls -r -m "/" -o 2048 /cases/case-2024-001/images/evidence.dd > /cases/case-2024-001/bodyfile.txt # Generate timeline from bodyfile mactime -b /cases/case-2024-001/bodyfile.txt -d > /cases/case-2024-001/timeline.csv # Filter timeline to specific date range mactime -b /cases/case-2024-001/bodyfile.txt \ -d 2024-01-15..2024-01-20 > /cases/case-2024-001/incident_timeline.csv
Key Concepts
| Concept | Description |
|---|---|
| Ingest Modules | Automated analysis plugins that process data sources upon import |
| MFT (Master File Table) | NTFS metadata structure recording all file entries and attributes |
| File carving | Recovering files from unallocated space using file signatures |
| Hash filtering | Using NSRL or custom hash sets to exclude known-good or flag known-bad files |
| Timeline analysis | Chronological reconstruction of file system and user activity events |
| Deleted file recovery | Restoring files whose directory entries are removed but data remains |
| Keyword indexing | Full-text search index built from all file content including slack space |
| Artifact extraction | Automated parsing of browser, email, registry, and OS-specific artifacts |
Tools & Systems
| Tool | Purpose |
|---|---|
| Autopsy | Open-source GUI forensic platform for disk image analysis |
| The Sleuth Kit (TSK) | Command-line forensic toolkit underlying Autopsy |
| fls | List files and directories in a disk image including deleted entries |
| icat | Extract file content by inode number from a disk image |
| mactime | Generate timeline from TSK bodyfile format |
| mmls | Display partition layout of a disk image |
| NSRL | NIST hash database for identifying known software files |
| sigfind | Search for file signatures at the sector level |
Common Scenarios
Scenario 1: Employee Data Theft Investigation Import the employee workstation image, run all ingest modules, search for company-confidential file names and keywords, examine USB connection artifacts in Recent Activity, check for cloud storage client artifacts, review deleted files for evidence of data staging, generate HTML report for legal team.
Scenario 2: Malware Infection Forensics Add the compromised system image, enable Extension Mismatch and Encryption Detection modules, examine the prefetch directory for execution evidence, search for known malware hashes, build timeline around the infection window, extract suspicious executables for further analysis in a sandbox.
Scenario 3: Child Exploitation Material (CSAM) Investigation Import image with PhotoDNA and Project VIC hash sets enabled, run Picture Analyzer module, hash all image files against known-bad databases, tag and categorize matches by severity, generate law enforcement report with chain of custody documentation.
Scenario 4: Intellectual Property Dispute Import multiple employee disk images as separate data sources in one case, perform keyword searches for proprietary terms and project names, compare file hashes between sources, build timeline showing file access and transfer patterns, export evidence for legal review.
Output Format
Autopsy Case Analysis Summary: Case: CASE-2024-001-Workstation Image: evidence.dd (500GB NTFS) Partitions: 2 (System Reserved + Primary) Total Files: 245,832 Deleted Files: 12,456 (recoverable: 8,234) Ingest Results: Hash Matches (Known Bad): 3 files Extension Mismatches: 17 files Keyword Hits: 234 across 45 files Encrypted Files: 5 containers detected EXIF Data Extracted: 1,245 images with metadata Tagged Evidence: Critical: 12 items Supporting: 34 items Review: 67 items Timeline Events: 1,234,567 entries (filtered to incident window: 892) Report: /cases/case-2024-001/reports/autopsy_report.html
