Autopsy aide-mémoire
Overview
Autopsy is a comprehensive criminalistique numérique platform that provides a graphical interface for The Sleuth Kit (TSK) and other criminalistique numérique tools. Developed by Basis Technology, Autopsy serves as the de facto standard for criminalistique numérique investigations in law enforcement, corporate security, and réponse aux incidents scenarios. The platform combines powerful forensic analysis capabilities with an intuitive user interface, making advanced criminalistique numérique techniques accessible to investigators of varying skill levels.
The core strength of Autopsy lies in its ability to processus 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 commande-line roots to become a sophisticated forensic workstation capable of handling complex investigations. The platform includes advanced features such as cléword 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 criminalistique numérique laboratories and réponse aux incidents teams.
Installation
Windows Installation
Installing Autopsy on Windows systems:
# 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 préférences
Linux Installation
Installing Autopsy on Linux distributions:
# 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
# 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://localhôte:9999/autopsy
Virtual Machine Setup
# 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 utilisation
Case Creation
Creating and managing forensic cases:
# Start Autopsy
autopsy
# Create new case via web interface
# Navigate to http://localhôte:9999/autopsy
# Case creation paramètres
Case Name: "Investigation_2024_001"
Case Directory: "/cases/investigation_001"
Investigator: "John Doe"
Description: "logiciel malveillant incident investigation"
# Add data source
# Select image file or physical device
# Choose processusing options
Data Source Analysis
Adding and analyzing data sources:
# Add disk image
# File -> Add Data Source
# Select disk image file (.dd, .raw, .E01)
# Add logical files
# Select directory or individual files
# Useful for cibleed analysis
# Add unallocated space
# Analyze free space and deleted files
# Recover deleted data
# Configure ingest modules
# Enable relevant analysis modules
# hash calculation, cléword search, etc.
File System Analysis
Analyzing file systems and recovering data:
# 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
cléword Searching
Implementing comprehensive cléword searches:
# Configure cléword lists
# Tools -> options -> cléword Search
# Create custom cléword 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 cléword hits
# Examine context around matches
# Export search results
hash Analysis
Performing hash analysis for known file identification:
# 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 logiciel malveillant 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
# drapeau potentially malicious files
# Prioritize analysis efforts
Email Analysis
Analyzing email artifacts and communications:
# 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
# cléword 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:
# 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 mot de passes
# Identify malicious websites
Automation Scripts
Batch Case processusing
#!/usr/bin/env python3
# Autopsy batch case processusing script
import os
import subprocessus
import json
import time
from datetime import datetime
class AutopsyBatchprocessusor:
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 commande-line script
script_content = f"""
# Autopsy batch processusing 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.setprocessusUnallocatedSpace(True)
ingestJobSettings.setprocessusKnownFilesFilter(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 = subprocessus.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 subprocessus.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 clé artifacts from completed case"""
artifacts = \\\\{\\\\}
# Define artifact paths
artifact_paths = \\\\{
"timeline": "timeline.csv",
"cléword_hits": "cléword_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.clés()),
"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 processus_evidence_batch(self, evidence_list):
"""processus multiple evidence files"""
for i, evidence_file in enumerate(evidence_list):
case_name = f"batch_case_\\\\{i+1:03d\\\\}"
print(f"processusing 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 processused 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']),
"processusing_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 processusing completed: \\\\{batch_summary['successful_cases']\\\\}/\\\\{batch_summary['total_cases']\\\\} successful")
# utilisation
if __name__ == "__main__":
processusor = AutopsyBatchprocessusor()
evidence_files = [
"/evidence/disk_image_1.dd",
"/evidence/disk_image_2.E01",
"/evidence/memory_dump.raw"
]
processusor.processus_evidence_batch(evidence_files)
Automated Artifact Extraction
#!/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_cléword_hits(self):
"""Extract cléword 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)
cléword_hits = []
for row in cursor.fetchall():
cléword_hits.append(\\\\{
"artifact_id": row[0],
"cléword": row[1],
"filename": row[2],
"file_path": row[3]
\\\\})
conn.close()
return cléword_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(),
"cléword_hits": self.extract_cléword_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].clés())
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
# utilisation
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
#!/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",
"cléword_hits": "cléword_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, clé=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_cléword_hits(self):
"""Analyze cléword search results"""
cléword_data = self.report_data.get("cléword_hits", [])
if not cléword_data:
return \\\\{\\\\}
# Group by cléword
cléword_counts = \\\\{\\\\}
for hit in cléword_data:
cléword = hit.get("cléword", "")
cléword_counts[cléword] = cléword_counts.get(cléword, 0) + 1
return \\\\{
"total_cléword_hits": len(cléword_data),
"unique_cléwords": len(cléword_counts),
"top_cléwords": sorted(cléword_counts.items(), clé=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()
cléword_analysis = self.analyze_cléword_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 criminalistique numérique 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>\\\\{\\\\{ cléword_analysis.total_cléword_hits \\\\}\\\\}</h3>
<p>cléword 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>cléword Analysis</h2>
<p>Total cléword hits: <span class="artifact-count">\\\\{\\\\{ cléword_analysis.total_cléword_hits \\\\}\\\\}</span></p>
<p>Unique cléwords: <span class="artifact-count">\\\\{\\\\{ cléword_analysis.unique_cléwords \\\\}\\\\}</span></p>
\\\\{% if cléword_analysis.top_cléwords %\\\\}
<h3>Top cléwords</h3>
<table>
<tr><th>cléword</th><th>Occurrences</th></tr>
\\\\{% for cléword, count in cléword_analysis.top_cléwords %\\\\}
<tr><td>\\\\{\\\\{ cléword \\\\}\\\\}</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>cléword Hits</td><td>\\\\{\\\\{ cléword_analysis.total_cléword_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,
cléword_analysis=cléword_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\\\\}")
# utilisation
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 exemples
SIEM Integration
#!/usr/bin/env python3
# Autopsy SIEM integration
import json
import requests
from datetime import datetime
class AutopsySIEMIntegration:
def __init__(self, siem_endpoint, api_clé):
self.siem_endpoint = siem_endpoint
self.api_clé = api_clé
self.headers = \\\\{
"autorisation": f"Bearer \\\\{api_clé\\\\}",
"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\\\\}")
# utilisation
siem_integration = AutopsySIEMIntegration("https://siem.company.com/api", "api_clé")
# siem_integration.send_artifacts_to_siem(extracted_artifacts)
dépannage
Common Issues
Database connexion Issues:
# 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:
# Increase Java heap size
export JAVA_OPTS="-Xmx8g -Xms4g"
# Monitor memory utilisation
top -p $(pgrep java)
# Check disk space
df -h /cases
# Optimize case database
sqlite3 /cases/case.db "VACUUM;"
Module Loading Issues:
# 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:
# Enable debug logging
autopsy --debug --log-level DEBUG
# Monitor case processusing
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 protocoles - Regular integrity verification procedures
Data Protection: - Encrypt case databases and evidence files - Implement Contrôle d'Accèss and authentification - Secure backup and recovery procedures - Monitor for unauthorized access attempts - Regular security assessments of forensic infrastructure
Legal and Compliance
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