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Autopsie Cheat Blatt

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Überblick

Autopsy ist eine umfassende digitale Forensik-Plattform, die eine grafische Schnittstelle für The Sleuth Kit (TSK) und andere digitale Forensics-Tools bietet. Entwickelt von Basis Technology dient Autopsy als De-facto-Standard für digitale Forensik-Untersuchungen in Strafverfolgung, Corporate Security und Notfall-Reaktionsszenarien. Die Plattform kombiniert leistungsstarke forensische Analysefunktionen mit einer intuitiven Benutzeroberfläche, wodurch fortgeschrittene digitale Forensik-Techniken für Ermittler unterschiedlicher Fähigkeiten zugänglich gemacht werden.

Die Kernkraft von Autopsy liegt in der Fähigkeit, verschiedene Arten von digitalen Beweisen zu verarbeiten und zu analysieren, einschließlich Festplattenbilder, Speicherdemps, mobile Geräteextraktionen und Netzwerkpaket-Erfassungen. Die Plattform unterstützt mehrere Dateisysteme (NTFS, FAT, ext⅔/4, HFS+) und kann gelöschte Dateien wiederherstellen, Datei-Metadaten analysieren, Artefakte aus Anwendungen und Timeline-Analysen extrahieren. Die modulare Architektur von Autopsy ermöglicht die Erweiterbarkeit durch Plugins und ermöglicht es den Ermittlern, die Plattform für spezifische Untersuchungsanforderungen anzupassen.

Autopsy hat sich deutlich von seinen ursprünglichen Kommandozeilenwurzeln entwickelt, um eine anspruchsvolle forensische Workstation zu werden, die komplexe Untersuchungen bewältigen kann. Die Plattform umfasst erweiterte Funktionen wie Keyword-Suche, Hash-Analyse für bekannte Datei-Identifizierung, E-Mail-Analyse, Web Artefakt-Extraktion und umfassende Reporting-Funktionen. Seine Integration mit anderen forensischen Werkzeugen und Datenbanken macht es zu einem wesentlichen Bestandteil moderner digitaler Forensik-Labors und Notfall-Reaktionsteams.

Installation

Windows Installation

Installation von Autopsy auf Windows-Systemen:

```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

Autopsy auf Linux-Distributionen installieren:

```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 & ```_

Basisnutzung

Fallerstellung

Erstellung und Verwaltung von forensischen Fällen:

```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

```_

Datenquellenanalyse

Hinzufügen und Analyse von Datenquellen:

```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.

```_

Dateisystemanalyse

Analyse von Dateisystemen und Wiederherstellung von Daten:

```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

```_

Erweiterte Funktionen

Schlüsselwort Suche

Durchführung umfassender Suchbegriffe:

```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-Analyse

Hash-Analyse zur bekannten Dateiidentifikation durchführen:

```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

```_

E-Mail senden

Analyse von E-Mail-Artefakte und Kommunikation:

```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 Artefakte Analyse

Extraktion und Analyse von Web-Browsing Artefakte:

```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

```_

Automatisierungsskripte

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)

```_

Automatisierte Artefakte Extraktion

```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"))

```_

Bericht 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 = """
Autopsy Forensic Analysis Report

Digital Forensic Analysis Report

Case Directory: \\\\{\\\\{ case_dir \\\\}\\\\}

Report Generated: \\\\{\\\\{ report_time \\\\}\\\\}

Analysis Tool: Autopsy Digital Forensics Platform

\\\\{\\\\{ timeline_analysis.total_files \\\\}\\\\}

Total Files Analyzed

\\\\{\\\\{ web_analysis.unique_urls \\\\}\\\\}

Unique URLs Found

\\\\{\\\\{ keyword_analysis.total_keyword_hits \\\\}\\\\}

Keyword Hits

Timeline Analysis

Total files with timestamps: \\\\{\\\\{ timeline_analysis.files_with_timestamps \\\\}\\\\}

\\\\{% if timeline_analysis.peak_activity_hour %\\\\}

Peak activity hour: \\\\{\\\\{ timeline_analysis.peak_activity_hour \\\\}\\\\}:00

\\\\{% endif %\\\\}

Web Activity Analysis

Total web artifacts: \\\\{\\\\{ web_analysis.total_web_artifacts \\\\}\\\\}

Unique domains visited: \\\\{\\\\{ web_analysis.unique_domains \\\\}\\\\}

\\\\{% if web_analysis.top_domains %\\\\}

Top Visited Domains

    \\\\{% for domain in web_analysis.top_domains %\\\\}
  • \\\\{\\\\{ domain \\\\}\\\\}
    • \\\\{% endfor %\\\\}
\\\\{% endif %\\\\}

Keyword Analysis

Total keyword hits: \\\\{\\\\{ keyword_analysis.total_keyword_hits \\\\}\\\\}

Unique keywords: \\\\{\\\\{ keyword_analysis.unique_keywords \\\\}\\\\}

\\\\{% if keyword_analysis.top_keywords %\\\\}

Top Keywords

\\\\{% for keyword, count in keyword_analysis.top_keywords %\\\\} \\\\{% endfor %\\\\}
KeywordOccurrences
\\\\{\\\\{ keyword \\\\}\\\\}\\\\{\\\\{ count \\\\}\\\\}
\\\\{% endif %\\\\}

Artifact Summary

Artifact TypeCount
Timeline Events\\\\{\\\\{ timeline_analysis.total_files \\\\}\\\\}
Web Artifacts\\\\{\\\\{ web_analysis.total_web_artifacts \\\\}\\\\}
Email Artifacts\\\\{\\\\{ report_data.email_artifacts|length \\\\}\\\\}
Keyword Hits\\\\{\\\\{ keyword_analysis.total_keyword_hits \\\\}\\\\}
Hash Hits\\\\{\\\\{ report_data.hash_hits|length \\\\}\\\\}
    """

    # 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)

```_

Integrationsbeispiele

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)

```_

Fehlerbehebung

Gemeinsame Themen

** Probleme der Datenbankverbindung:** ```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;" ```_

Modul Belastungsprobleme: ```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

Debugging und Protokollierung aktivieren:

```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 ```_

Sicherheitsüberlegungen

Nachweis Integrity

Chain of Custody: - Alle Nachweise für die Verarbeitung - Detaillierte Protokolle von Zugriffen und Änderungen - Verwenden Sie kryptographische Hashes, um Integrität zu überprüfen - Durchführung richtiger Beweisspeicherprotokolle - Regelmäßige Überprüfung der Integrität

Datenschutz: - Verschlüsseln von Falldatenbanken und Beweisdateien - Implementierung von Zugriffskontrollen und Authentifizierung - Sichere Sicherungs- und Rückforderungsverfahren - Monitor für unberechtigte Zugriffsversuche - Regelmäßige Sicherheitsbewertungen der forensischen Infrastruktur

Recht und Compliance

Rechtsvorschriften: - Befolgen Sie geltende Gesetze und Vorschriften - ordnungsgemäße Dokumentation und Aufzeichnungen - Durchführung defensibler forensischer Verfahren - Gewährleistung der Zulässigkeit digitaler Beweise - Regelmäßige Schulung der gesetzlichen Anforderungen

** Datenschutzhinweise:** - Respektieren Sie Datenschutzrechte und Vorschriften - Implementierung von Datenminimierungsprinzipien - Sicheres Handling personenbezogener Daten - Richtige Aufbewahrung und Entsorgung von Daten - Einhaltung der Datenschutzgesetze

Referenzen

  1. [Autopsy Digital Forensics Platform](https://LINK_5
  2. The Sleuth Kit Documentation
  3. Digital Forensics Best Practices
  4. [NIST Computer Forensics Guidelines](LINK_5_
  5. [Digital Evidence Standards](LINK_5