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Autopsy Cheat Sheet ​

Overview ​

Autopsy is a comprehensive digital forensics platform that provides a graphical interface for The Sleuth Kit (TSK) and other digital forensics tools. Developed by Basis Technology, Autopsy serves as the de facto standard for digital forensics investigations in law enforcement, corporate security, and incident response scenarios. The platform combines powerful forensic analysis capabilities with an intuitive user interface, making advanced digital forensics techniques accessible to investigators of varying skill levels.

The core strength of Autopsy lies in its ability to process and analyze various types of digital evidence, including disk images, memory dumps, mobile device extractions, and network packet captures. The platform supports multiple file systems (NTFS, FAT, ext2/3/4, HFS+) and can recover deleted files, analyze file metadata, extract artifacts from applications, and timeline analysis. Autopsy's modular architecture allows for extensibility through plugins, enabling investigators to customize the platform for specific investigation requirements.

Autopsy has evolved significantly from its original command-line roots to become a sophisticated forensic workstation capable of handling complex investigations. The platform includes advanced features such as keyword searching, hash analysis for known file identification, email analysis, web artifact extraction, and comprehensive reporting capabilities. Its integration with other forensic tools and databases makes it an essential component of modern digital forensics laboratories and incident response teams.

Installation ​

Windows Installation ​

Installing Autopsy on Windows systems:

bash
# Download Autopsy installer
# Visit: https://www.autopsy.com/download/

# Run installer as administrator
autopsy-4.20.0-64bit.msi

# Verify installation
"C:\Program Files\Autopsy-4.20.0\bin\autopsy64.exe" --version

# Install additional dependencies
# Java 8+ (included with installer)
# Microsoft Visual C++ Redistributable

# Configure Autopsy
# Launch Autopsy
# Configure case directory
# Set up user preferences

Linux Installation ​

Installing Autopsy on Linux distributions:

bash
# Ubuntu/Debian installation
sudo apt update
sudo apt install autopsy sleuthkit

# Install dependencies
sudo apt install openjdk-8-jdk testdisk photorec

# Download latest Autopsy
wget https://github.com/sleuthkit/autopsy/releases/download/autopsy-4.20.0/autopsy-4.20.0.zip

# Extract and install
unzip autopsy-4.20.0.zip
cd autopsy-4.20.0

# Run installation script
sudo ./unix_setup.sh

# Start Autopsy
./bin/autopsy

Docker Installation ​

bash
# Create Autopsy Docker environment
cat > Dockerfile << 'EOF'
FROM ubuntu:20.04
ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y \
    autopsy sleuthkit openjdk-8-jdk \
    testdisk photorec ewf-tools \
    libewf-dev python3 python3-pip

WORKDIR /cases
EXPOSE 9999

CMD ["autopsy"]
EOF

# Build container
docker build -t autopsy-forensics .

# Run with case directory mounted
docker run -it -p 9999:9999 -v $(pwd)/cases:/cases autopsy-forensics

# Access web interface
# http://localhost:9999/autopsy

Virtual Machine Setup ​

bash
# Download forensics VM with Autopsy
# SANS SIFT Workstation
wget https://digital-forensics.sans.org/community/downloads

# Import VM
VBoxManage import SIFT-Workstation.ova

# Configure VM resources
VBoxManage modifyvm "SIFT" --memory 8192 --cpus 4

# Start VM
VBoxManage startvm "SIFT"

# Access Autopsy
autopsy &

Basic Usage ​

Case Creation ​

Creating and managing forensic cases:

bash
# Start Autopsy
autopsy

# Create new case via web interface
# Navigate to http://localhost:9999/autopsy

# Case creation parameters
Case Name: "Investigation_2024_001"
Case Directory: "/cases/investigation_001"
Investigator: "John Doe"
Description: "Malware incident investigation"

# Add data source
# Select image file or physical device
# Choose processing options

Data Source Analysis ​

Adding and analyzing data sources:

bash
# Add disk image
# File -> Add Data Source
# Select disk image file (.dd, .raw, .E01)

# Add logical files
# Select directory or individual files
# Useful for targeted analysis

# Add unallocated space
# Analyze free space and deleted files
# Recover deleted data

# Configure ingest modules
# Enable relevant analysis modules
# Hash calculation, keyword search, etc.

File System Analysis ​

Analyzing file systems and recovering data:

bash
# Browse file system
# Navigate directory structure
# View file metadata and properties

# Recover deleted files
# Check "Deleted Files" node
# Analyze file signatures
# Recover based on file headers

# Timeline analysis
# Generate timeline of file activity
# Correlate events across time
# Identify suspicious patterns

# File type analysis
# Analyze files by type
# Identify misnamed files
# Check file signatures

Advanced Features ​

Keyword Searching ​

Implementing comprehensive keyword searches:

bash
# Configure keyword lists
# Tools -> Options -> Keyword Search

# Create custom keyword lists
# Add specific terms related to investigation
# Include regular expressions

# Search configuration
Search Type: "Exact Match" or "Regular Expression"
Encoding: "UTF-8", "UTF-16", "ASCII"
Language: "English" (for indexing)

# Advanced search options
# Case sensitive search
# Whole word matching
# Search in slack space
# Search unallocated space

# Search results analysis
# Review keyword hits
# Examine context around matches
# Export search results

Hash Analysis ​

Performing hash analysis for known file identification:

bash
# Configure hash databases
# Tools -> Options -> Hash Database

# Import NSRL database
# Download from https://www.nist.gov/itl/ssd/software-quality-group/nsrl-download
# Import hash sets for known good files

# Import custom hash sets
# Create hash sets for known bad files
# Import malware hash databases
# Add organization-specific hash sets

# Hash calculation
# Enable "Hash Lookup" ingest module
# Calculate MD5, SHA-1, SHA-256 hashes
# Compare against known hash databases

# Notable files identification
# Identify unknown files
# Flag potentially malicious files
# Prioritize analysis efforts

Email Analysis ​

Analyzing email artifacts and communications:

bash
# Email artifact extraction
# Enable "Email Parser" ingest module
# Support for PST, OST, MBOX formats
# Extract email metadata and content

# Email analysis features
# View email headers and routing
# Analyze attachments
# Extract embedded images
# Timeline email communications

# Advanced email analysis
# Keyword search in email content
# Identify email patterns
# Analyze sender/recipient relationships
# Export email evidence

# Webmail analysis
# Extract webmail artifacts from browsers
# Analyze cached email content
# Recover deleted webmail messages

Web Artifact Analysis ​

Extracting and analyzing web browsing artifacts:

bash
# Web artifact extraction
# Enable "Recent Activity" ingest module
# Extract browser history, cookies, downloads
# Analyze cached web content

# Browser support
# Chrome, Firefox, Internet Explorer
# Safari, Edge browsers
# Mobile browser artifacts

# Analysis capabilities
# Timeline web activity
# Identify visited websites
# Analyze search queries
# Extract form data

# Advanced web analysis
# Recover deleted browser history
# Analyze private browsing artifacts
# Extract stored passwords
# Identify malicious websites

Automation Scripts ​

Batch Case Processing ​

python
#!/usr/bin/env python3
# Autopsy batch case processing script

import os
import subprocess
import json
import time
from datetime import datetime

class AutopsyBatchProcessor:
    def __init__(self, autopsy_path="/opt/autopsy/bin/autopsy"):
        self.autopsy_path = autopsy_path
        self.cases_dir = "/cases"
        self.results = {}
    
    def create_case(self, case_name, evidence_file, investigator="Automated"):
        """Create new Autopsy case"""
        case_dir = os.path.join(self.cases_dir, case_name)
        
        # Create case directory
        os.makedirs(case_dir, exist_ok=True)
        
        # Case configuration
        case_config = {
            "case_name": case_name,
            "case_dir": case_dir,
            "investigator": investigator,
            "created": datetime.now().isoformat(),
            "evidence_file": evidence_file
        }
        
        # Save case configuration
        with open(os.path.join(case_dir, "case_config.json"), "w") as f:
            json.dump(case_config, f, indent=2)
        
        return case_config
    
    def run_autopsy_analysis(self, case_config):
        """Run Autopsy analysis on case"""
        
        # Create Autopsy command-line script
        script_content = f"""
# Autopsy batch processing script
import org.sleuthkit.autopsy.casemodule.Case
import org.sleuthkit.autopsy.coreutils.Logger
import org.sleuthkit.autopsy.ingest.IngestManager

# Create case
case = Case.createAsCurrentCase(
    Case.CaseType.SINGLE_USER_CASE,
    "{case_config['case_name']}",
    "{case_config['case_dir']}",
    Case.CaseDetails("{case_config['case_name']}", "{case_config['investigator']}", "", "", "")
)

# Add data source
dataSource = case.addDataSource("{case_config['evidence_file']}")

# Configure ingest modules
ingestJobSettings = IngestJobSettings()
ingestJobSettings.setProcessUnallocatedSpace(True)
ingestJobSettings.setProcessKnownFilesFilter(True)

# Start ingest
ingestManager = IngestManager.getInstance()
ingestJob = ingestManager.beginIngestJob(dataSource, ingestJobSettings)

# Wait for completion
while ingestJob.getStatus() != IngestJob.Status.COMPLETED:
    time.sleep(30)

print("Analysis completed for case: {case_config['case_name']}")
"""
        
        # Save script
        script_file = os.path.join(case_config['case_dir'], "analysis_script.py")
        with open(script_file, "w") as f:
            f.write(script_content)
        
        # Run Autopsy with script
        cmd = [
            self.autopsy_path,
            "--script", script_file,
            "--case-dir", case_config['case_dir']
        ]
        
        try:
            result = subprocess.run(cmd, capture_output=True, text=True, timeout=7200)
            
            if result.returncode == 0:
                return {
                    "status": "success",
                    "output": result.stdout,
                    "completion_time": datetime.now().isoformat()
                }
            else:
                return {
                    "status": "failed",
                    "error": result.stderr,
                    "completion_time": datetime.now().isoformat()
                }
                
        except subprocess.TimeoutExpired:
            return {
                "status": "timeout",
                "completion_time": datetime.now().isoformat()
            }
        except Exception as e:
            return {
                "status": "error",
                "error": str(e),
                "completion_time": datetime.now().isoformat()
            }
    
    def extract_artifacts(self, case_dir):
        """Extract key artifacts from completed case"""
        artifacts = {}
        
        # Define artifact paths
        artifact_paths = {
            "timeline": "timeline.csv",
            "keyword_hits": "keyword_hits.csv",
            "hash_analysis": "hash_analysis.csv",
            "web_artifacts": "web_artifacts.csv",
            "email_artifacts": "email_artifacts.csv"
        }
        
        for artifact_type, filename in artifact_paths.items():
            artifact_file = os.path.join(case_dir, "Reports", filename)
            
            if os.path.exists(artifact_file):
                artifacts[artifact_type] = artifact_file
                print(f"Found {artifact_type}: {artifact_file}")
        
        return artifacts
    
    def generate_summary_report(self, case_config, analysis_result, artifacts):
        """Generate case summary report"""
        
        report = {
            "case_info": case_config,
            "analysis_result": analysis_result,
            "artifacts_found": list(artifacts.keys()),
            "report_generated": datetime.now().isoformat()
        }
        
        # Add artifact statistics
        for artifact_type, artifact_file in artifacts.items():
            try:
                with open(artifact_file, 'r') as f:
                    lines = f.readlines()
                    report[f"{artifact_type}_count"] = len(lines) - 1  # Exclude header
            except:
                report[f"{artifact_type}_count"] = 0
        
        # Save report
        report_file = os.path.join(case_config['case_dir'], "summary_report.json")
        with open(report_file, "w") as f:
            json.dump(report, f, indent=2)
        
        return report
    
    def process_evidence_batch(self, evidence_list):
        """Process multiple evidence files"""
        
        for i, evidence_file in enumerate(evidence_list):
            case_name = f"batch_case_{i+1:03d}"
            
            print(f"Processing case {i+1}/{len(evidence_list)}: {case_name}")
            
            # Create case
            case_config = self.create_case(case_name, evidence_file)
            
            # Run analysis
            analysis_result = self.run_autopsy_analysis(case_config)
            
            # Extract artifacts
            artifacts = self.extract_artifacts(case_config['case_dir'])
            
            # Generate report
            summary = self.generate_summary_report(case_config, analysis_result, artifacts)
            
            # Store results
            self.results[case_name] = summary
            
            print(f"Completed case: {case_name}")
        
        # Generate batch summary
        self.generate_batch_summary()
    
    def generate_batch_summary(self):
        """Generate summary of all processed cases"""
        
        batch_summary = {
            "total_cases": len(self.results),
            "successful_cases": len([r for r in self.results.values() if r['analysis_result']['status'] == 'success']),
            "failed_cases": len([r for r in self.results.values() if r['analysis_result']['status'] != 'success']),
            "processing_time": datetime.now().isoformat(),
            "cases": self.results
        }
        
        with open(os.path.join(self.cases_dir, "batch_summary.json"), "w") as f:
            json.dump(batch_summary, f, indent=2)
        
        print(f"Batch processing completed: {batch_summary['successful_cases']}/{batch_summary['total_cases']} successful")

# Usage
if __name__ == "__main__":
    processor = AutopsyBatchProcessor()
    
    evidence_files = [
        "/evidence/disk_image_1.dd",
        "/evidence/disk_image_2.E01",
        "/evidence/memory_dump.raw"
    ]
    
    processor.process_evidence_batch(evidence_files)

Automated Artifact Extraction ​

python
#!/usr/bin/env python3
# Automated artifact extraction from Autopsy cases

import sqlite3
import csv
import json
import os
from datetime import datetime

class AutopsyArtifactExtractor:
    def __init__(self, case_db_path):
        self.case_db_path = case_db_path
        self.artifacts = {}
    
    def connect_to_case_db(self):
        """Connect to Autopsy case database"""
        try:
            conn = sqlite3.connect(self.case_db_path)
            return conn
        except Exception as e:
            print(f"Error connecting to case database: {e}")
            return None
    
    def extract_timeline_artifacts(self):
        """Extract timeline artifacts"""
        conn = self.connect_to_case_db()
        if not conn:
            return []
        
        query = """
        SELECT 
            tsk_files.name,
            tsk_files.crtime,
            tsk_files.mtime,
            tsk_files.atime,
            tsk_files.ctime,
            tsk_files.size,
            tsk_files.parent_path
        FROM tsk_files
        WHERE tsk_files.meta_type = 1
        ORDER BY tsk_files.crtime
        """
        
        cursor = conn.cursor()
        cursor.execute(query)
        
        timeline_data = []
        for row in cursor.fetchall():
            timeline_data.append({
                "filename": row[0],
                "created": row[1],
                "modified": row[2],
                "accessed": row[3],
                "changed": row[4],
                "size": row[5],
                "path": row[6]
            })
        
        conn.close()
        return timeline_data
    
    def extract_web_artifacts(self):
        """Extract web browsing artifacts"""
        conn = self.connect_to_case_db()
        if not conn:
            return []
        
        query = """
        SELECT 
            blackboard_artifacts.artifact_id,
            blackboard_attributes.value_text,
            blackboard_attributes.value_int32,
            blackboard_attributes.value_int64,
            blackboard_attribute_types.display_name
        FROM blackboard_artifacts
        JOIN blackboard_attributes ON blackboard_artifacts.artifact_id = blackboard_attributes.artifact_id
        JOIN blackboard_attribute_types ON blackboard_attributes.attribute_type_id = blackboard_attribute_types.attribute_type_id
        WHERE blackboard_artifacts.artifact_type_id IN (1, 2, 3, 4, 5)
        """
        
        cursor = conn.cursor()
        cursor.execute(query)
        
        web_artifacts = []
        for row in cursor.fetchall():
            web_artifacts.append({
                "artifact_id": row[0],
                "value_text": row[1],
                "value_int32": row[2],
                "value_int64": row[3],
                "attribute_type": row[4]
            })
        
        conn.close()
        return web_artifacts
    
    def extract_email_artifacts(self):
        """Extract email artifacts"""
        conn = self.connect_to_case_db()
        if not conn:
            return []
        
        query = """
        SELECT 
            blackboard_artifacts.artifact_id,
            blackboard_attributes.value_text,
            blackboard_attribute_types.display_name
        FROM blackboard_artifacts
        JOIN blackboard_attributes ON blackboard_artifacts.artifact_id = blackboard_attributes.artifact_id
        JOIN blackboard_attribute_types ON blackboard_attributes.attribute_type_id = blackboard_attribute_types.attribute_type_id
        WHERE blackboard_artifacts.artifact_type_id = 12
        """
        
        cursor = conn.cursor()
        cursor.execute(query)
        
        email_artifacts = []
        for row in cursor.fetchall():
            email_artifacts.append({
                "artifact_id": row[0],
                "content": row[1],
                "attribute_type": row[2]
            })
        
        conn.close()
        return email_artifacts
    
    def extract_keyword_hits(self):
        """Extract keyword search hits"""
        conn = self.connect_to_case_db()
        if not conn:
            return []
        
        query = """
        SELECT 
            blackboard_artifacts.artifact_id,
            blackboard_attributes.value_text,
            tsk_files.name,
            tsk_files.parent_path
        FROM blackboard_artifacts
        JOIN blackboard_attributes ON blackboard_artifacts.artifact_id = blackboard_attributes.artifact_id
        JOIN tsk_files ON blackboard_artifacts.obj_id = tsk_files.obj_id
        WHERE blackboard_artifacts.artifact_type_id = 9
        """
        
        cursor = conn.cursor()
        cursor.execute(query)
        
        keyword_hits = []
        for row in cursor.fetchall():
            keyword_hits.append({
                "artifact_id": row[0],
                "keyword": row[1],
                "filename": row[2],
                "file_path": row[3]
            })
        
        conn.close()
        return keyword_hits
    
    def extract_hash_hits(self):
        """Extract hash analysis results"""
        conn = self.connect_to_case_db()
        if not conn:
            return []
        
        query = """
        SELECT 
            tsk_files.name,
            tsk_files.md5,
            tsk_files.sha256,
            tsk_files.parent_path,
            tsk_files.size
        FROM tsk_files
        WHERE tsk_files.known = 2
        """
        
        cursor = conn.cursor()
        cursor.execute(query)
        
        hash_hits = []
        for row in cursor.fetchall():
            hash_hits.append({
                "filename": row[0],
                "md5": row[1],
                "sha256": row[2],
                "path": row[3],
                "size": row[4]
            })
        
        conn.close()
        return hash_hits
    
    def export_artifacts_to_csv(self, output_dir):
        """Export all artifacts to CSV files"""
        
        os.makedirs(output_dir, exist_ok=True)
        
        # Extract all artifact types
        artifacts = {
            "timeline": self.extract_timeline_artifacts(),
            "web_artifacts": self.extract_web_artifacts(),
            "email_artifacts": self.extract_email_artifacts(),
            "keyword_hits": self.extract_keyword_hits(),
            "hash_hits": self.extract_hash_hits()
        }
        
        # Export to CSV
        for artifact_type, data in artifacts.items():
            if data:
                csv_file = os.path.join(output_dir, f"{artifact_type}.csv")
                
                with open(csv_file, 'w', newline='') as f:
                    if data:
                        writer = csv.DictWriter(f, fieldnames=data[0].keys())
                        writer.writeheader()
                        writer.writerows(data)
                
                print(f"Exported {len(data)} {artifact_type} to {csv_file}")
        
        return artifacts
    
    def generate_artifact_summary(self, artifacts, output_file):
        """Generate summary of extracted artifacts"""
        
        summary = {
            "extraction_time": datetime.now().isoformat(),
            "case_database": self.case_db_path,
            "artifact_counts": {
                artifact_type: len(data) for artifact_type, data in artifacts.items()
            },
            "total_artifacts": sum(len(data) for data in artifacts.values())
        }
        
        with open(output_file, 'w') as f:
            json.dump(summary, f, indent=2)
        
        print(f"Artifact summary saved to {output_file}")
        return summary

# Usage
if __name__ == "__main__":
    case_db = "/cases/investigation_001/case.db"
    output_dir = "/cases/investigation_001/extracted_artifacts"
    
    extractor = AutopsyArtifactExtractor(case_db)
    artifacts = extractor.export_artifacts_to_csv(output_dir)
    summary = extractor.generate_artifact_summary(artifacts, os.path.join(output_dir, "summary.json"))

Report Generation ​

python
#!/usr/bin/env python3
# Autopsy report generation script

import os
import json
import csv
from datetime import datetime
from jinja2 import Template

class AutopsyReportGenerator:
    def __init__(self, case_dir):
        self.case_dir = case_dir
        self.artifacts_dir = os.path.join(case_dir, "extracted_artifacts")
        self.report_data = {}
    
    def load_artifact_data(self):
        """Load extracted artifact data"""
        
        artifact_files = {
            "timeline": "timeline.csv",
            "web_artifacts": "web_artifacts.csv",
            "email_artifacts": "email_artifacts.csv",
            "keyword_hits": "keyword_hits.csv",
            "hash_hits": "hash_hits.csv"
        }
        
        for artifact_type, filename in artifact_files.items():
            file_path = os.path.join(self.artifacts_dir, filename)
            
            if os.path.exists(file_path):
                with open(file_path, 'r') as f:
                    reader = csv.DictReader(f)
                    self.report_data[artifact_type] = list(reader)
            else:
                self.report_data[artifact_type] = []
    
    def analyze_timeline_data(self):
        """Analyze timeline data for patterns"""
        timeline_data = self.report_data.get("timeline", [])
        
        if not timeline_data:
            return {}
        
        # Analyze file creation patterns
        creation_times = [item["created"] for item in timeline_data if item["created"]]
        
        # Group by hour
        hourly_activity = {}
        for timestamp in creation_times:
            try:
                hour = datetime.fromisoformat(timestamp).hour
                hourly_activity[hour] = hourly_activity.get(hour, 0) + 1
            except:
                continue
        
        return {
            "total_files": len(timeline_data),
            "files_with_timestamps": len(creation_times),
            "peak_activity_hour": max(hourly_activity, key=hourly_activity.get) if hourly_activity else None,
            "hourly_distribution": hourly_activity
        }
    
    def analyze_web_activity(self):
        """Analyze web browsing activity"""
        web_data = self.report_data.get("web_artifacts", [])
        
        if not web_data:
            return {}
        
        # Extract URLs and domains
        urls = []
        domains = set()
        
        for artifact in web_data:
            if artifact.get("attribute_type") == "TSK_URL":
                url = artifact.get("value_text", "")
                if url:
                    urls.append(url)
                    try:
                        domain = url.split("//")[1].split("/")[0]
                        domains.add(domain)
                    except:
                        continue
        
        return {
            "total_web_artifacts": len(web_data),
            "unique_urls": len(set(urls)),
            "unique_domains": len(domains),
            "top_domains": list(domains)[:10]
        }
    
    def analyze_keyword_hits(self):
        """Analyze keyword search results"""
        keyword_data = self.report_data.get("keyword_hits", [])
        
        if not keyword_data:
            return {}
        
        # Group by keyword
        keyword_counts = {}
        for hit in keyword_data:
            keyword = hit.get("keyword", "")
            keyword_counts[keyword] = keyword_counts.get(keyword, 0) + 1
        
        return {
            "total_keyword_hits": len(keyword_data),
            "unique_keywords": len(keyword_counts),
            "top_keywords": sorted(keyword_counts.items(), key=lambda x: x[1], reverse=True)[:10]
        }
    
    def generate_html_report(self, output_file):
        """Generate comprehensive HTML report"""
        
        # Load artifact data
        self.load_artifact_data()
        
        # Perform analysis
        timeline_analysis = self.analyze_timeline_data()
        web_analysis = self.analyze_web_activity()
        keyword_analysis = self.analyze_keyword_hits()
        
        # HTML template
        html_template = """
<!DOCTYPE html>
<html>
<head>
    <title>Autopsy Forensic Analysis Report</title>
    <style>
        body { font-family: Arial, sans-serif; margin: 20px; }
        .header { background-color: #f0f0f0; padding: 20px; border-radius: 5px; }
        .section { margin: 20px 0; padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
        .artifact-count { font-weight: bold; color: #2c5aa0; }
        table { width: 100%; border-collapse: collapse; margin: 10px 0; }
        th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }
        th { background-color: #f2f2f2; }
        .summary-stats { display: flex; justify-content: space-around; margin: 20px 0; }
        .stat-box { text-align: center; padding: 15px; background-color: #e8f4f8; border-radius: 5px; }
    </style>
</head>
<body>
    <div class="header">
        <h1>Digital Forensic Analysis Report</h1>
        <p><strong>Case Directory:</strong> {{ case_dir }}</p>
        <p><strong>Report Generated:</strong> {{ report_time }}</p>
        <p><strong>Analysis Tool:</strong> Autopsy Digital Forensics Platform</p>
    </div>

    <div class="summary-stats">
        <div class="stat-box">
            <h3>{{ timeline_analysis.total_files }}</h3>
            <p>Total Files Analyzed</p>
        </div>
        <div class="stat-box">
            <h3>{{ web_analysis.unique_urls }}</h3>
            <p>Unique URLs Found</p>
        </div>
        <div class="stat-box">
            <h3>{{ keyword_analysis.total_keyword_hits }}</h3>
            <p>Keyword Hits</p>
        </div>
    </div>

    <div class="section">
        <h2>Timeline Analysis</h2>
        <p>Total files with timestamps: <span class="artifact-count">{{ timeline_analysis.files_with_timestamps }}</span></p>
        {% if timeline_analysis.peak_activity_hour %}
        <p>Peak activity hour: <span class="artifact-count">{{ timeline_analysis.peak_activity_hour }}:00</span></p>
        {% endif %}
    </div>

    <div class="section">
        <h2>Web Activity Analysis</h2>
        <p>Total web artifacts: <span class="artifact-count">{{ web_analysis.total_web_artifacts }}</span></p>
        <p>Unique domains visited: <span class="artifact-count">{{ web_analysis.unique_domains }}</span></p>
        
        {% if web_analysis.top_domains %}
        <h3>Top Visited Domains</h3>
        <ul>
        {% for domain in web_analysis.top_domains %}
            <li>{{ domain }}</li>
        {% endfor %}
        </ul>
        {% endif %}
    </div>

    <div class="section">
        <h2>Keyword Analysis</h2>
        <p>Total keyword hits: <span class="artifact-count">{{ keyword_analysis.total_keyword_hits }}</span></p>
        <p>Unique keywords: <span class="artifact-count">{{ keyword_analysis.unique_keywords }}</span></p>
        
        {% if keyword_analysis.top_keywords %}
        <h3>Top Keywords</h3>
        <table>
            <tr><th>Keyword</th><th>Occurrences</th></tr>
            {% for keyword, count in keyword_analysis.top_keywords %}
            <tr><td>{{ keyword }}</td><td>{{ count }}</td></tr>
            {% endfor %}
        </table>
        {% endif %}
    </div>

    <div class="section">
        <h2>Artifact Summary</h2>
        <table>
            <tr><th>Artifact Type</th><th>Count</th></tr>
            <tr><td>Timeline Events</td><td>{{ timeline_analysis.total_files }}</td></tr>
            <tr><td>Web Artifacts</td><td>{{ web_analysis.total_web_artifacts }}</td></tr>
            <tr><td>Email Artifacts</td><td>{{ report_data.email_artifacts|length }}</td></tr>
            <tr><td>Keyword Hits</td><td>{{ keyword_analysis.total_keyword_hits }}</td></tr>
            <tr><td>Hash Hits</td><td>{{ report_data.hash_hits|length }}</td></tr>
        </table>
    </div>
</body>
</html>
        """
        
        # Render template
        template = Template(html_template)
        html_content = template.render(
            case_dir=self.case_dir,
            report_time=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            timeline_analysis=timeline_analysis,
            web_analysis=web_analysis,
            keyword_analysis=keyword_analysis,
            report_data=self.report_data
        )
        
        # Save report
        with open(output_file, 'w') as f:
            f.write(html_content)
        
        print(f"HTML report generated: {output_file}")

# Usage
if __name__ == "__main__":
    case_dir = "/cases/investigation_001"
    output_file = os.path.join(case_dir, "forensic_report.html")
    
    generator = AutopsyReportGenerator(case_dir)
    generator.generate_html_report(output_file)

Integration Examples ​

SIEM Integration ​

python
#!/usr/bin/env python3
# Autopsy SIEM integration

import json
import requests
from datetime import datetime

class AutopsySIEMIntegration:
    def __init__(self, siem_endpoint, api_key):
        self.siem_endpoint = siem_endpoint
        self.api_key = api_key
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
    
    def send_artifacts_to_siem(self, artifacts):
        """Send Autopsy artifacts to SIEM"""
        
        for artifact_type, data in artifacts.items():
            for item in data:
                siem_event = self.format_for_siem(artifact_type, item)
                self.send_event(siem_event)
    
    def format_for_siem(self, artifact_type, artifact_data):
        """Format artifact data for SIEM ingestion"""
        
        base_event = {
            "timestamp": datetime.now().isoformat(),
            "source": "autopsy",
            "artifact_type": artifact_type,
            "event_type": "forensic_artifact"
        }
        
        # Add artifact-specific data
        base_event.update(artifact_data)
        
        return base_event
    
    def send_event(self, event_data):
        """Send event to SIEM"""
        
        try:
            response = requests.post(
                f"{self.siem_endpoint}/events",
                headers=self.headers,
                json=event_data
            )
            
            if response.status_code == 200:
                print(f"Event sent successfully: {event_data['artifact_type']}")
            else:
                print(f"Failed to send event: {response.status_code}")
                
        except Exception as e:
            print(f"Error sending event to SIEM: {e}")

# Usage
siem_integration = AutopsySIEMIntegration("https://siem.company.com/api", "api_key")
# siem_integration.send_artifacts_to_siem(extracted_artifacts)

Troubleshooting ​

Common Issues ​

Database Connection Issues:

bash
# Check case database integrity
sqlite3 /cases/case.db "PRAGMA integrity_check;"

# Repair corrupted database
sqlite3 /cases/case.db ".recover" | sqlite3 /cases/case_recovered.db

# Check database permissions
ls -la /cases/case.db
chmod 644 /cases/case.db

Memory and Performance Issues:

bash
# Increase Java heap size
export JAVA_OPTS="-Xmx8g -Xms4g"

# Monitor memory usage
top -p $(pgrep java)

# Check disk space
df -h /cases

# Optimize case database
sqlite3 /cases/case.db "VACUUM;"

Module Loading Issues:

bash
# Check module dependencies
autopsy --check-modules

# Verify Python modules
python3 -c "import autopsy_modules"

# Check log files
tail -f /var/log/autopsy/autopsy.log

# Reset module configuration
rm -rf ~/.autopsy/modules

Debugging ​

Enable detailed debugging and logging:

bash
# Enable debug logging
autopsy --debug --log-level DEBUG

# Monitor case processing
tail -f /cases/case.log

# Check ingest module status
autopsy --status --case /cases/investigation_001

# Verify evidence integrity
md5sum /evidence/disk_image.dd

Security Considerations ​

Evidence Integrity ​

Chain of Custody:

  • Document all evidence handling procedures
  • Maintain detailed logs of access and modifications
  • Use cryptographic hashes to verify integrity
  • Implement proper evidence storage protocols
  • Regular integrity verification procedures

Data Protection:

  • Encrypt case databases and evidence files
  • Implement access controls and authentication
  • Secure backup and recovery procedures
  • Monitor for unauthorized access attempts
  • Regular security assessments of forensic infrastructure

Legal Requirements:

  • Follow applicable laws and regulations
  • Maintain proper documentation and records
  • Implement defensible forensic procedures
  • Ensure admissibility of digital evidence
  • Regular training on legal requirements

Privacy Considerations:

  • Respect privacy rights and regulations
  • Implement data minimization principles
  • Secure handling of personal information
  • Proper data retention and disposal
  • Compliance with data protection laws

References ​

  1. Autopsy Digital Forensics Platform
  2. The Sleuth Kit Documentation
  3. Digital Forensics Best Practices
  4. NIST Computer Forensics Guidelines
  5. Digital Evidence Standards