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Maltego Cheat Sheet ​

Overview ​

Maltego is a comprehensive open source intelligence (OSINT) and graphical link analysis tool developed by Paterva that enables investigators to gather, analyze, and visualize relationships between people, organizations, domains, IP addresses, and other entities. The platform excels at transforming disparate pieces of information into actionable intelligence through its unique graph-based visualization approach, making complex relationships and patterns immediately apparent to analysts. Maltego has become the industry standard for OSINT investigations, used extensively by law enforcement, intelligence agencies, corporate security teams, and penetration testers worldwide.

The core strength of Maltego lies in its transform system, which automates the process of gathering information from various data sources and presenting it in an intuitive graphical format. Transforms are automated queries that take an input entity (such as a domain name) and return related entities (such as IP addresses, email addresses, or associated domains) along with their relationships. This automation capability allows investigators to rapidly expand their understanding of a target's digital footprint while maintaining visual context of how different pieces of information connect to each other.

Maltego's versatility extends beyond traditional OSINT investigations to include threat intelligence analysis, fraud investigation, digital forensics support, and social network analysis. The platform supports both manual investigation techniques and automated data mining, making it suitable for both targeted investigations and broad intelligence gathering operations. Its ability to integrate with numerous data sources through transforms, combined with its powerful visualization engine, makes Maltego an indispensable tool for any organization requiring comprehensive intelligence analysis capabilities.

Installation ​

Windows Installation ​

Installing Maltego on Windows systems:

bash
# Download Maltego installer
# Visit: https://www.maltego.com/downloads/

# Run installer as administrator
maltego-4.6.0-windows.exe

# Choose installation directory
# Default: C:\Program Files\Maltego

# Configure Java settings
# Minimum 4GB RAM recommended
# Java 8 or higher required

# Launch Maltego
# Start Menu -> Maltego

# Initial setup
# Create Maltego account
# Choose license type (Community/Commercial)
# Configure transform hub

Linux Installation ​

Installing Maltego on Linux distributions:

bash
# Download Linux installer
wget https://downloads.maltego.com/maltego-v4/linux/Maltego.v4.6.0.deb

# Install on Ubuntu/Debian
sudo dpkg -i Maltego.v4.6.0.deb
sudo apt-get install -f

# Install dependencies if needed
sudo apt install openjdk-11-jre

# Alternative: Download tar.gz
wget https://downloads.maltego.com/maltego-v4/linux/Maltego.v4.6.0.linux.zip
unzip Maltego.v4.6.0.linux.zip
cd Maltego.v4.6.0

# Make executable
chmod +x bin/maltego

# Run Maltego
./bin/maltego

# Create desktop shortcut
cp maltego.desktop ~/.local/share/applications/

macOS Installation ​

bash
# Download macOS installer
# Visit: https://www.maltego.com/downloads/

# Install DMG package
# Drag Maltego to Applications folder

# Install Java if needed
brew install openjdk@11

# Launch from Applications
# Or from terminal
/Applications/Maltego.app/Contents/MacOS/maltego

# Configure security settings
# System Preferences -> Security & Privacy
# Allow Maltego to run

Docker Installation ​

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

RUN apt-get update && apt-get install -y \
    openjdk-11-jre wget unzip \
    xvfb x11vnc fluxbox

# Download and install Maltego
RUN wget https://downloads.maltego.com/maltego-v4/linux/Maltego.v4.6.0.linux.zip
RUN unzip Maltego.v4.6.0.linux.zip
RUN mv Maltego.v4.6.0 /opt/maltego

# Setup VNC
EXPOSE 5900

# Start script
COPY start.sh /start.sh
RUN chmod +x /start.sh

CMD ["/start.sh"]
EOF

# Create start script
cat > start.sh << 'EOF'
#!/bin/bash
Xvfb :1 -screen 0 1024x768x16 &
export DISPLAY=:1
fluxbox &
x11vnc -display :1 -nopw -listen localhost -xkb &
cd /opt/maltego
./bin/maltego
EOF

# Build and run
docker build -t maltego-osint .
docker run -p 5900:5900 maltego-osint

Basic Usage ​

Initial Setup ​

Setting up Maltego for first use:

bash
# Launch Maltego
maltego

# Account Setup
# 1. Create Maltego account or login
# 2. Choose license type:
#    - Community Edition (free, limited)
#    - Commercial License (full features)

# Transform Hub Configuration
# 1. Access Transform Hub
# 2. Install transform packages:
#    - Standard Transforms
#    - Social Networks
#    - Threat Intelligence
#    - Infrastructure

# Machine Configuration
# 1. Configure machines for automated investigations
# 2. Set up API keys for data sources
# 3. Configure proxy settings if needed

# Workspace Setup
# 1. Create new graph
# 2. Configure entity palette
# 3. Set up custom entity types if needed

Basic Investigation ​

Performing basic OSINT investigations:

bash
# Starting an Investigation
# 1. Create new graph
# 2. Add initial entity (domain, person, email)
# 3. Right-click entity -> Run Transform

# Domain Investigation
# Add Domain entity: example.com
# Run transforms:
# - To DNS Name [DNS]
# - To IP Address [DNS]
# - To MX Record [DNS]
# - To Website [HTTP]

# Email Investigation
# Add Email Address entity: user@example.com
# Run transforms:
# - To Domain [Email]
# - To Person [Email]
# - To Social Network Profile

# Person Investigation
# Add Person entity: John Doe
# Run transforms:
# - To Email Address [Search Engines]
# - To Social Network Profile
# - To Phone Number
# - To Address

Entity Management ​

Working with entities and relationships:

bash
# Adding Entities
# 1. Drag from Entity Palette
# 2. Right-click graph -> Add Entity
# 3. Import from file (CSV, XML)

# Entity Properties
# 1. Select entity
# 2. View Properties tab
# 3. Edit entity details
# 4. Add custom properties

# Relationship Management
# 1. Select two entities
# 2. Right-click -> Add Link
# 3. Choose link type
# 4. Set link properties

# Entity Grouping
# 1. Select multiple entities
# 2. Right-click -> Group
# 3. Name the group
# 4. Set group properties

Advanced Features ​

Custom Transforms ​

Creating and configuring custom transforms:

bash
# Transform Development
# 1. Access Transform Hub
# 2. Create new transform
# 3. Configure input/output entities
# 4. Write transform code

# Python Transform Example
#!/usr/bin/env python3
from maltego_trx.maltego import UIM_TYPES
from maltego_trx.transform import DiscoverableTransform

class DomainToSubdomains(DiscoverableTransform):
    @classmethod
    def create_entities(cls, request, response):
        domain = request.Value
        
        # Custom subdomain discovery logic
        subdomains = discover_subdomains(domain)
        
        for subdomain in subdomains:
            response.addEntity("maltego.Domain", subdomain)
        
        return response

def discover_subdomains(domain):
    # Implementation for subdomain discovery
    # Using DNS queries, certificate transparency, etc.
    pass

# Transform Configuration
# 1. Set transform name and description
# 2. Configure input entity types
# 3. Set output entity types
# 4. Configure authentication if needed
# 5. Test transform functionality

Machine Configuration ​

Setting up automated investigation machines:

bash
# Creating Investigation Machines
# 1. Access Machines tab
# 2. Create new machine
# 3. Add transform sequence
# 4. Configure parameters

# Domain Investigation Machine
# Sequence:
# 1. Domain -> DNS Names
# 2. Domain -> IP Addresses
# 3. Domain -> MX Records
# 4. Domain -> Subdomains
# 5. IP Address -> Geolocation
# 6. IP Address -> Network Block

# Person Investigation Machine
# Sequence:
# 1. Person -> Email Addresses
# 2. Email -> Domains
# 3. Person -> Social Profiles
# 4. Person -> Phone Numbers
# 5. Phone -> Location

# Machine Parameters
# - Maximum entities per transform
# - Transform timeout settings
# - API rate limiting
# - Output filtering rules

Data Integration ​

Integrating external data sources:

bash
# API Integration
# 1. Configure API keys in Transform Hub
# 2. Available APIs:
#    - VirusTotal
#    - Shodan
#    - PassiveTotal
#    - ThreatCrowd
#    - Social media APIs

# Custom Data Sources
# 1. Create CSV import templates
# 2. Configure entity mappings
# 3. Import data files
# 4. Validate imported entities

# Database Integration
# 1. Configure database connections
# 2. Create SQL-based transforms
# 3. Map database fields to entities
# 4. Set up automated queries

# File Import/Export
# Supported formats:
# - CSV (entities and links)
# - XML (Maltego format)
# - GraphML
# - JSON
# - KML (geolocation data)

Advanced Visualization ​

Advanced graph visualization and analysis:

bash
# Layout Algorithms
# 1. Hierarchical Layout
# 2. Circular Layout
# 3. Organic Layout
# 4. Force-Directed Layout
# 5. Geographic Layout

# Filtering and Selection
# 1. Filter by entity type
# 2. Filter by property values
# 3. Filter by link types
# 4. Time-based filtering
# 5. Custom filter expressions

# Graph Analysis
# 1. Centrality analysis
# 2. Clustering detection
# 3. Path analysis
# 4. Network metrics
# 5. Pattern recognition

# Visualization Customization
# 1. Entity icons and colors
# 2. Link styles and colors
# 3. Label customization
# 4. Size scaling by properties
# 5. Conditional formatting

Automation Scripts ​

Automated OSINT Collection ​

python
#!/usr/bin/env python3
# Automated OSINT collection using Maltego API

import requests
import json
import time
from datetime import datetime

class MaltegoOSINTCollector:
    def __init__(self, api_key, base_url="https://api.maltego.com"):
        self.api_key = api_key
        self.base_url = base_url
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        self.results = {}
    
    def create_graph(self, name, description=""):
        """Create new investigation graph"""
        data = {
            "name": name,
            "description": description,
            "created": datetime.now().isoformat()
        }
        
        response = requests.post(
            f"{self.base_url}/graphs",
            headers=self.headers,
            json=data
        )
        
        if response.status_code == 201:
            graph_data = response.json()
            return graph_data["id"]
        else:
            print(f"Error creating graph: {response.status_code}")
            return None
    
    def add_entity(self, graph_id, entity_type, value, properties=None):
        """Add entity to graph"""
        data = {
            "type": entity_type,
            "value": value,
            "properties": properties or {}
        }
        
        response = requests.post(
            f"{self.base_url}/graphs/{graph_id}/entities",
            headers=self.headers,
            json=data
        )
        
        if response.status_code == 201:
            return response.json()["id"]
        else:
            print(f"Error adding entity: {response.status_code}")
            return None
    
    def run_transform(self, graph_id, entity_id, transform_name):
        """Run transform on entity"""
        data = {
            "transform": transform_name,
            "entity_id": entity_id
        }
        
        response = requests.post(
            f"{self.base_url}/graphs/{graph_id}/transforms",
            headers=self.headers,
            json=data
        )
        
        if response.status_code == 200:
            return response.json()
        else:
            print(f"Error running transform: {response.status_code}")
            return None
    
    def run_machine(self, graph_id, entity_id, machine_name):
        """Run investigation machine"""
        data = {
            "machine": machine_name,
            "entity_id": entity_id,
            "max_entities": 100,
            "timeout": 300
        }
        
        response = requests.post(
            f"{self.base_url}/graphs/{graph_id}/machines",
            headers=self.headers,
            json=data
        )
        
        if response.status_code == 200:
            job_id = response.json()["job_id"]
            return self.wait_for_job(job_id)
        else:
            print(f"Error running machine: {response.status_code}")
            return None
    
    def wait_for_job(self, job_id, timeout=600):
        """Wait for job completion"""
        start_time = time.time()
        
        while time.time() - start_time < timeout:
            response = requests.get(
                f"{self.base_url}/jobs/{job_id}",
                headers=self.headers
            )
            
            if response.status_code == 200:
                job_data = response.json()
                status = job_data["status"]
                
                if status == "completed":
                    return job_data["results"]
                elif status == "failed":
                    print(f"Job failed: {job_data.get('error', 'Unknown error')}")
                    return None
                else:
                    time.sleep(10)  # Wait 10 seconds before checking again
            else:
                print(f"Error checking job status: {response.status_code}")
                return None
        
        print("Job timeout")
        return None
    
    def investigate_domain(self, domain):
        """Comprehensive domain investigation"""
        print(f"Starting investigation of domain: {domain}")
        
        # Create graph
        graph_id = self.create_graph(f"Domain Investigation: {domain}")
        
        if not graph_id:
            return None
        
        # Add domain entity
        entity_id = self.add_entity(graph_id, "maltego.Domain", domain)
        
        if not entity_id:
            return None
        
        # Run domain investigation machine
        results = self.run_machine(graph_id, entity_id, "Domain Investigation")
        
        if results:
            self.results[domain] = {
                "graph_id": graph_id,
                "investigation_results": results,
                "timestamp": datetime.now().isoformat()
            }
            
            print(f"Investigation completed for {domain}")
            return results
        else:
            print(f"Investigation failed for {domain}")
            return None
    
    def investigate_email(self, email):
        """Comprehensive email investigation"""
        print(f"Starting investigation of email: {email}")
        
        # Create graph
        graph_id = self.create_graph(f"Email Investigation: {email}")
        
        if not graph_id:
            return None
        
        # Add email entity
        entity_id = self.add_entity(graph_id, "maltego.EmailAddress", email)
        
        if not entity_id:
            return None
        
        # Run email investigation transforms
        transforms = [
            "To Domain [Email]",
            "To Person [Email]",
            "To Social Network Profile",
            "To Breach Data"
        ]
        
        results = {}
        for transform in transforms:
            print(f"Running transform: {transform}")
            result = self.run_transform(graph_id, entity_id, transform)
            if result:
                results[transform] = result
            time.sleep(2)  # Rate limiting
        
        self.results[email] = {
            "graph_id": graph_id,
            "investigation_results": results,
            "timestamp": datetime.now().isoformat()
        }
        
        return results
    
    def investigate_person(self, person_name):
        """Comprehensive person investigation"""
        print(f"Starting investigation of person: {person_name}")
        
        # Create graph
        graph_id = self.create_graph(f"Person Investigation: {person_name}")
        
        if not graph_id:
            return None
        
        # Add person entity
        entity_id = self.add_entity(graph_id, "maltego.Person", person_name)
        
        if not entity_id:
            return None
        
        # Run person investigation machine
        results = self.run_machine(graph_id, entity_id, "Person Investigation")
        
        if results:
            self.results[person_name] = {
                "graph_id": graph_id,
                "investigation_results": results,
                "timestamp": datetime.now().isoformat()
            }
            
            print(f"Investigation completed for {person_name}")
            return results
        else:
            print(f"Investigation failed for {person_name}")
            return None
    
    def export_results(self, output_file="maltego_results.json"):
        """Export investigation results"""
        with open(output_file, 'w') as f:
            json.dump(self.results, f, indent=2)
        
        print(f"Results exported to {output_file}")
    
    def generate_report(self, output_file="maltego_report.html"):
        """Generate HTML investigation report"""
        
        html_template = """
<!DOCTYPE html>
<html>
<head>
    <title>Maltego OSINT Investigation Report</title>
    <style>
        body { font-family: Arial, sans-serif; margin: 20px; }
        .header { background-color: #f0f0f0; padding: 20px; }
        .investigation { margin: 20px 0; border: 1px solid #ccc; padding: 15px; }
        .entity { margin: 10px 0; padding: 10px; background-color: #f9f9f9; }
        .results { margin: 10px 0; }
    </style>
</head>
<body>
    <div class="header">
        <h1>Maltego OSINT Investigation Report</h1>
        <p>Generated: {timestamp}</p>
        <p>Total Investigations: {total_investigations}</p>
    </div>
    
    {investigations}
</body>
</html>
        """
        
        investigations_html = ""
        for target, data in self.results.items():
            investigations_html += f"""
            <div class="investigation">
                <h2>Investigation: {target}</h2>
                <p>Timestamp: {data['timestamp']}</p>
                <p>Graph ID: {data['graph_id']}</p>
                <div class="results">
                    <h3>Results Summary</h3>
                    <p>Entities discovered: {len(data.get('investigation_results', {}))}</p>
                </div>
            </div>
            """
        
        html_content = html_template.format(
            timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            total_investigations=len(self.results),
            investigations=investigations_html
        )
        
        with open(output_file, 'w') as f:
            f.write(html_content)
        
        print(f"Report generated: {output_file}")

# Usage
if __name__ == "__main__":
    collector = MaltegoOSINTCollector("your_api_key")
    
    # Investigate multiple targets
    targets = [
        ("domain", "example.com"),
        ("email", "user@example.com"),
        ("person", "John Doe")
    ]
    
    for target_type, target_value in targets:
        if target_type == "domain":
            collector.investigate_domain(target_value)
        elif target_type == "email":
            collector.investigate_email(target_value)
        elif target_type == "person":
            collector.investigate_person(target_value)
        
        time.sleep(5)  # Rate limiting between investigations
    
    # Export results
    collector.export_results()
    collector.generate_report()

Bulk Entity Processing ​

python
#!/usr/bin/env python3
# Bulk entity processing for Maltego

import csv
import json
import time
from datetime import datetime

class MaltegoBulkProcessor:
    def __init__(self, maltego_collector):
        self.collector = maltego_collector
        self.processed_entities = []
        self.failed_entities = []
    
    def process_csv_file(self, csv_file, entity_type_column, entity_value_column):
        """Process entities from CSV file"""
        
        with open(csv_file, 'r') as f:
            reader = csv.DictReader(f)
            
            for row in reader:
                entity_type = row[entity_type_column]
                entity_value = row[entity_value_column]
                
                print(f"Processing {entity_type}: {entity_value}")
                
                try:
                    if entity_type.lower() == "domain":
                        result = self.collector.investigate_domain(entity_value)
                    elif entity_type.lower() == "email":
                        result = self.collector.investigate_email(entity_value)
                    elif entity_type.lower() == "person":
                        result = self.collector.investigate_person(entity_value)
                    else:
                        print(f"Unknown entity type: {entity_type}")
                        continue
                    
                    if result:
                        self.processed_entities.append({
                            "type": entity_type,
                            "value": entity_value,
                            "status": "success",
                            "timestamp": datetime.now().isoformat()
                        })
                    else:
                        self.failed_entities.append({
                            "type": entity_type,
                            "value": entity_value,
                            "status": "failed",
                            "timestamp": datetime.now().isoformat()
                        })
                
                except Exception as e:
                    print(f"Error processing {entity_value}: {e}")
                    self.failed_entities.append({
                        "type": entity_type,
                        "value": entity_value,
                        "status": "error",
                        "error": str(e),
                        "timestamp": datetime.now().isoformat()
                    })
                
                # Rate limiting
                time.sleep(10)
    
    def generate_processing_report(self, output_file="bulk_processing_report.json"):
        """Generate bulk processing report"""
        
        report = {
            "processing_summary": {
                "total_entities": len(self.processed_entities) + len(self.failed_entities),
                "successful": len(self.processed_entities),
                "failed": len(self.failed_entities),
                "success_rate": len(self.processed_entities) / (len(self.processed_entities) + len(self.failed_entities)) * 100
            },
            "processed_entities": self.processed_entities,
            "failed_entities": self.failed_entities,
            "report_timestamp": datetime.now().isoformat()
        }
        
        with open(output_file, 'w') as f:
            json.dump(report, f, indent=2)
        
        print(f"Bulk processing report saved: {output_file}")
        return report

# Usage
collector = MaltegoOSINTCollector("your_api_key")
processor = MaltegoBulkProcessor(collector)

# Process entities from CSV
processor.process_csv_file("entities.csv", "type", "value")

# Generate report
processor.generate_processing_report()

Transform Development Framework ​

python
#!/usr/bin/env python3
# Maltego transform development framework

from maltego_trx.maltego import UIM_TYPES
from maltego_trx.transform import DiscoverableTransform
import requests
import dns.resolver
import whois
import socket

class CustomDomainTransforms:
    """Collection of custom domain-related transforms"""
    
    class DomainToSubdomains(DiscoverableTransform):
        @classmethod
        def create_entities(cls, request, response):
            domain = request.Value
            
            # Certificate Transparency subdomain discovery
            subdomains = cls.discover_subdomains_ct(domain)
            
            for subdomain in subdomains:
                entity = response.addEntity("maltego.Domain", subdomain)
                entity.addProperty("source", "Certificate Transparency")
            
            return response
        
        @staticmethod
        def discover_subdomains_ct(domain):
            """Discover subdomains using Certificate Transparency"""
            subdomains = set()
            
            try:
                url = f"https://crt.sh/?q=%.{domain}&output=json"
                response = requests.get(url, timeout=30)
                
                if response.status_code == 200:
                    data = response.json()
                    
                    for cert in data:
                        name_value = cert.get('name_value', '')
                        for name in name_value.split('\n'):
                            name = name.strip()
                            if name.endswith(f'.{domain}') and '*' not in name:
                                subdomains.add(name)
            
            except Exception as e:
                print(f"Error discovering subdomains: {e}")
            
            return list(subdomains)
    
    class DomainToTechnologies(DiscoverableTransform):
        @classmethod
        def create_entities(cls, request, response):
            domain = request.Value
            
            # Technology detection
            technologies = cls.detect_technologies(domain)
            
            for tech in technologies:
                entity = response.addEntity("maltego.Website", tech['name'])
                entity.addProperty("technology.version", tech.get('version', ''))
                entity.addProperty("technology.confidence", tech.get('confidence', ''))
            
            return response
        
        @staticmethod
        def detect_technologies(domain):
            """Detect web technologies"""
            technologies = []
            
            try:
                # Simple technology detection based on headers
                response = requests.get(f"http://{domain}", timeout=10)
                headers = response.headers
                
                # Server detection
                server = headers.get('Server', '')
                if server:
                    technologies.append({
                        'name': server,
                        'type': 'Web Server',
                        'confidence': 'high'
                    })
                
                # Framework detection
                x_powered_by = headers.get('X-Powered-By', '')
                if x_powered_by:
                    technologies.append({
                        'name': x_powered_by,
                        'type': 'Framework',
                        'confidence': 'high'
                    })
                
                # Content analysis
                content = response.text.lower()
                
                # WordPress detection
                if 'wp-content' in content or 'wordpress' in content:
                    technologies.append({
                        'name': 'WordPress',
                        'type': 'CMS',
                        'confidence': 'high'
                    })
                
                # jQuery detection
                if 'jquery' in content:
                    technologies.append({
                        'name': 'jQuery',
                        'type': 'JavaScript Library',
                        'confidence': 'medium'
                    })
            
            except Exception as e:
                print(f"Error detecting technologies: {e}")
            
            return technologies
    
    class DomainToWhoisInfo(DiscoverableTransform):
        @classmethod
        def create_entities(cls, request, response):
            domain = request.Value
            
            # WHOIS lookup
            whois_info = cls.get_whois_info(domain)
            
            if whois_info:
                # Add registrar
                if 'registrar' in whois_info:
                    entity = response.addEntity("maltego.Organization", whois_info['registrar'])
                    entity.addProperty("organization.type", "Registrar")
                
                # Add registrant
                if 'registrant' in whois_info:
                    entity = response.addEntity("maltego.Person", whois_info['registrant'])
                    entity.addProperty("person.role", "Registrant")
                
                # Add creation date
                if 'creation_date' in whois_info:
                    entity = response.addEntity("maltego.Date", str(whois_info['creation_date']))
                    entity.addProperty("date.type", "Domain Creation")
            
            return response
        
        @staticmethod
        def get_whois_info(domain):
            """Get WHOIS information"""
            try:
                w = whois.whois(domain)
                
                return {
                    'registrar': w.registrar,
                    'registrant': w.registrant,
                    'creation_date': w.creation_date,
                    'expiration_date': w.expiration_date,
                    'name_servers': w.name_servers
                }
            
            except Exception as e:
                print(f"Error getting WHOIS info: {e}")
                return None

# Transform registration
def register_transforms():
    """Register custom transforms"""
    transforms = [
        CustomDomainTransforms.DomainToSubdomains,
        CustomDomainTransforms.DomainToTechnologies,
        CustomDomainTransforms.DomainToWhoisInfo
    ]
    
    return transforms

# Usage in Maltego
if __name__ == "__main__":
    # This would be called by Maltego transform runner
    transforms = register_transforms()
    print(f"Registered {len(transforms)} custom transforms")

Integration Examples ​

Threat Intelligence Integration ​

python
#!/usr/bin/env python3
# Maltego threat intelligence integration

import requests
import json

class ThreatIntelligenceIntegration:
    def __init__(self, maltego_collector):
        self.collector = maltego_collector
        self.threat_feeds = {
            "virustotal": "https://www.virustotal.com/vtapi/v2",
            "threatcrowd": "https://www.threatcrowd.org/searchApi/v2",
            "otx": "https://otx.alienvault.com/api/v1"
        }
    
    def enrich_with_threat_intel(self, graph_id, entity_id, entity_value, entity_type):
        """Enrich entity with threat intelligence"""
        
        threat_data = {}
        
        if entity_type == "domain":
            threat_data = self.check_domain_reputation(entity_value)
        elif entity_type == "ip":
            threat_data = self.check_ip_reputation(entity_value)
        elif entity_type == "hash":
            threat_data = self.check_file_reputation(entity_value)
        
        # Add threat intelligence entities to graph
        if threat_data:
            self.add_threat_entities(graph_id, entity_id, threat_data)
    
    def check_domain_reputation(self, domain):
        """Check domain reputation across threat feeds"""
        reputation_data = {}
        
        # VirusTotal domain report
        try:
            vt_response = requests.get(
                f"{self.threat_feeds['virustotal']}/domain/report",
                params={"apikey": "your_vt_api_key", "domain": domain}
            )
            
            if vt_response.status_code == 200:
                vt_data = vt_response.json()
                reputation_data["virustotal"] = {
                    "positives": vt_data.get("positives", 0),
                    "total": vt_data.get("total", 0),
                    "scan_date": vt_data.get("scan_date", "")
                }
        except Exception as e:
            print(f"Error checking VirusTotal: {e}")
        
        # ThreatCrowd domain report
        try:
            tc_response = requests.get(
                f"{self.threat_feeds['threatcrowd']}/domain/report",
                params={"domain": domain}
            )
            
            if tc_response.status_code == 200:
                tc_data = tc_response.json()
                reputation_data["threatcrowd"] = {
                    "votes": tc_data.get("votes", 0),
                    "references": tc_data.get("references", []),
                    "resolutions": tc_data.get("resolutions", [])
                }
        except Exception as e:
            print(f"Error checking ThreatCrowd: {e}")
        
        return reputation_data
    
    def add_threat_entities(self, graph_id, entity_id, threat_data):
        """Add threat intelligence entities to graph"""
        
        for source, data in threat_data.items():
            # Add threat intelligence source
            ti_entity_id = self.collector.add_entity(
                graph_id, 
                "maltego.ThreatIntelligence", 
                source,
                {"source": source, "data": json.dumps(data)}
            )
            
            # Link to original entity
            if ti_entity_id:
                self.collector.add_link(graph_id, entity_id, ti_entity_id, "threat_intelligence")

# Usage
collector = MaltegoOSINTCollector("your_api_key")
threat_intel = ThreatIntelligenceIntegration(collector)

# Investigate domain with threat intelligence
graph_id = collector.create_graph("Threat Intel Investigation")
entity_id = collector.add_entity(graph_id, "maltego.Domain", "suspicious-domain.com")
threat_intel.enrich_with_threat_intel(graph_id, entity_id, "suspicious-domain.com", "domain")

Social Media Integration ​

python
#!/usr/bin/env python3
# Maltego social media integration

import tweepy
import facebook
import linkedin

class SocialMediaIntegration:
    def __init__(self, maltego_collector, api_keys):
        self.collector = maltego_collector
        self.api_keys = api_keys
        self.setup_apis()
    
    def setup_apis(self):
        """Setup social media API connections"""
        
        # Twitter API
        if "twitter" in self.api_keys:
            auth = tweepy.OAuthHandler(
                self.api_keys["twitter"]["consumer_key"],
                self.api_keys["twitter"]["consumer_secret"]
            )
            auth.set_access_token(
                self.api_keys["twitter"]["access_token"],
                self.api_keys["twitter"]["access_token_secret"]
            )
            self.twitter_api = tweepy.API(auth)
        
        # Facebook API
        if "facebook" in self.api_keys:
            self.facebook_api = facebook.GraphAPI(
                access_token=self.api_keys["facebook"]["access_token"]
            )
    
    def search_social_profiles(self, person_name):
        """Search for social media profiles"""
        profiles = {}
        
        # Twitter search
        try:
            twitter_users = self.twitter_api.search_users(person_name, count=10)
            profiles["twitter"] = [
                {
                    "username": user.screen_name,
                    "name": user.name,
                    "followers": user.followers_count,
                    "verified": user.verified
                }
                for user in twitter_users
            ]
        except Exception as e:
            print(f"Error searching Twitter: {e}")
        
        return profiles
    
    def analyze_social_connections(self, username, platform):
        """Analyze social media connections"""
        connections = []
        
        if platform == "twitter":
            try:
                # Get followers
                followers = self.twitter_api.get_followers(screen_name=username, count=100)
                
                for follower in followers:
                    connections.append({
                        "type": "follower",
                        "username": follower.screen_name,
                        "name": follower.name,
                        "relationship": "follows"
                    })
            
            except Exception as e:
                print(f"Error analyzing Twitter connections: {e}")
        
        return connections

# Usage
api_keys = {
    "twitter": {
        "consumer_key": "your_consumer_key",
        "consumer_secret": "your_consumer_secret",
        "access_token": "your_access_token",
        "access_token_secret": "your_access_token_secret"
    }
}

collector = MaltegoOSINTCollector("your_api_key")
social_media = SocialMediaIntegration(collector, api_keys)

# Search for social profiles
profiles = social_media.search_social_profiles("John Doe")

Troubleshooting ​

Common Issues ​

Transform Errors:

bash
# Check transform configuration
# 1. Verify API keys are correct
# 2. Check transform permissions
# 3. Validate input entity types
# 4. Review transform logs

# Debug transform execution
# 1. Run transform manually
# 2. Check network connectivity
# 3. Verify data source availability
# 4. Review rate limiting settings

# Transform timeout issues
# 1. Increase timeout settings
# 2. Optimize transform code
# 3. Implement proper error handling
# 4. Add retry mechanisms

Performance Issues:

bash
# Optimize graph performance
# 1. Limit entity count per transform
# 2. Use filtering to reduce noise
# 3. Implement entity grouping
# 4. Regular graph cleanup

# Memory management
# 1. Close unused graphs
# 2. Clear transform cache
# 3. Restart Maltego periodically
# 4. Monitor system resources

# Network optimization
# 1. Configure proxy settings
# 2. Implement connection pooling
# 3. Use local data sources when possible
# 4. Optimize API usage patterns

Data Quality Issues:

bash
# Validate data sources
# 1. Check data freshness
# 2. Verify source reliability
# 3. Cross-reference multiple sources
# 4. Implement data validation

# Handle incomplete data
# 1. Implement fallback sources
# 2. Use data enrichment services
# 3. Manual verification processes
# 4. Document data limitations

Debugging ​

Enable detailed debugging and logging:

bash
# Enable Maltego debugging
# 1. Help -> Debug Information
# 2. Enable verbose logging
# 3. Check log files in:
#    - Windows: %APPDATA%\Maltego\logs
#    - Linux: ~/.maltego/logs
#    - macOS: ~/Library/Application Support/Maltego/logs

# Transform debugging
# 1. Add debug prints to transform code
# 2. Use transform testing framework
# 3. Monitor API responses
# 4. Check entity property validation

# Network debugging
# 1. Use network monitoring tools
# 2. Check proxy configurations
# 3. Verify SSL certificates
# 4. Monitor API rate limits

Security Considerations ​

Operational Security ​

Data Protection:

  • Encrypt sensitive investigation data
  • Use secure communication channels
  • Implement proper access controls
  • Regular security assessments
  • Secure disposal of investigation artifacts

API Security:

  • Secure storage of API keys
  • Regular key rotation
  • Monitor API usage patterns
  • Implement rate limiting
  • Use least privilege principles

Privacy Compliance:

  • Respect privacy laws and regulations
  • Obtain proper authorization for investigations
  • Implement data minimization principles
  • Secure handling of personal information
  • Regular compliance audits

Ethical OSINT:

  • Use only publicly available information
  • Respect terms of service
  • Avoid social engineering
  • Maintain professional standards
  • Document investigation methodology

References ​

  1. Maltego Official Website
  2. Maltego Documentation
  3. Maltego Transform Hub
  4. OSINT Framework
  5. Open Source Intelligence Techniques