ThreatSight: Enterprise Vulnerability Assessment Platform

From Nmap Wrapper to AI-Powered Threat Intelligence

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Project Vision

Bridging the gap between basic network scanning and enterprise-grade vulnerability management

• For: Security teams, DevOps engineers, and IT administrators
• Replaces: Manual Nmap analysis + Spreadsheet tracking
• Differentiator: Automated risk prioritization with business context and AI-powered threat intelligence

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Architecture Overview

graph TB
    A[ThreatSight Core] --> B[Scanning Engine]
    A --> C[Vulnerability Correlation]
    A --> D[Risk Intelligence]
    A --> E[Enterprise Integration]
    
    B --> B1[TCP/SYN Scanning]
    B --> B2[Protocol-Specific Analysis]
    B --> B3[Advanced Evasion Techniques]
    
    C --> C1[NVD API Integration]
    C --> C2[EPSS Scoring]
    C --> C3[Exploit-DB Matching]
    
    D --> D1[Asset Criticality]
    D --> D2[MITRE ATT&CK Mapping]
    D --> D3[Remediation Guidance]
    
    E --> E1[SIEM Integration]
    E --> E2[Ticketing Systems]
    E --> E3[Scheduled Scanning]

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Phase Roadmap

Phase 1: Advanced Scanning Engine (Current)

(Expected Completion: Month 2)

Features Implemented

def scan_target(target, scan_type="hybrid"):
    """Advanced scanning with multiple techniques"""
    if scan_type == "syn":
        return syn_scan(target)  # Raw socket SYN scanning
    elif scan_type == "stealth":
        return stealth_scan(target)  # Evasion techniques
    elif scan_type == "protocol":
        return protocol_specific_scan(target)  # Modbus/MQTT/etc

• Multi-threaded and asynchronous scanning architecture
• TCP Connect, SYN, and UDP scanning implementations
• Protocol-specific scanning (Modbus TCP, MQTT, HTTP/S)
• Advanced evasion techniques (source randomization, packet fragmentation)
• OS fingerprinting via TCP/IP stack analysis
• Intelligent banner grabbing with service-specific probes

Skills Required

• Advanced Python concurrency (asyncio, multiprocessing)
• Raw socket programming and packet crafting
• Protocol analysis (Industrial, IoT, Web)
• Evasion technique implementation

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Phase 2: AI-Powered Vulnerability Correlation

(Expected Completion: Month 4)

Planned Features

def analyze_vulnerabilities(scan_results):
    """AI-enhanced vulnerability assessment"""
    cves = nvd_api.lookup(scan_results)
    epss_scores = get_epss_prediction(cves)
    exploits = search_exploit_db(scan_results)
    
    return prioritize_vulnerabilities(cves, epss_scores, exploits)

• NVD API integration with local caching
• EPSS scoring for exploit prediction
• Exploit-DB and Metasploit integration
• Machine learning for false positive reduction
• CVSS v3.1 scoring with environmental metrics

Data Flow

flowchart LR
    Scan-->Services-->CVE_Matching-->EPSS_Scoring-->Exploit_Check-->Risk_Prioritization

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Skills to Learn

• REST API consumption (NVD, Vulners, Exploit-DB)
• Machine learning for security analytics
• Data caching with Redis
• CVSS vector parsing and environmental metrics

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Phase 3: Intelligent Risk Assessment

(Expected Completion: Month 6)

Key Components

class RiskIntelligenceEngine:
    def assess_risk(self, vulnerability, asset_context):
        """Context-aware risk assessment"""
        base_score = vulnerability.cvss_score
        asset_value = asset_context.get_criticality()
        exploit_probability = vulnerability.epss_score
        business_impact = self.calculate_business_impact()
        
        return (base_score * 0.4) + (asset_value * 0.3) + 
               (exploit_probability * 0.2) + (business_impact * 0.1)

• Asset criticality weighting with business context
• MITRE ATT&CK technique mapping
• Automated remediation guidance system
• Predictive analytics for emerging threats

Sample Output

[CRITICAL] Port 445/tcp - SMBv1 (CVE-2021-34527)
- CVSS: 9.8 | EPSS: 0.97 | Asset Criticality: High
- Attack Path: Initial Access → Lateral Movement
- MITRE Techniques: T1210, T1570
- Action: Disable SMBv1 immediately (Priority: P0)
- Estimated Remediation Time: 2 hours

Skills to Master

• Risk management frameworks (FAIR, NIST RMF)
• Pandas for security data analysis
• Report templating with Jinja2
• Business impact analysis techniques

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Phase 4: Enterprise Integration & Automation

(Expected Completion: Month 8)

Expansion Plans

def enterprise_workflow(scan_results):
    """Full enterprise integration pipeline"""
    tickets = jira.create_tickets(scan_results)
    splunk.ingest_data(scan_results)
    slack.send_alerts(high_risk_vulns)
    generate_compliance_reports(scan_results)

• JIRA/ServiceNow ticketing with automated prioritization
• SIEM integrations (Splunk HEC, Elasticsearch)
• ChatOps integration (Slack, Microsoft Teams)
• Scheduled scanning with APScheduler
• Compliance reporting (PCI DSS, HIPAA, ISO 27001)
• REST API for integration with other security tools

Architecture

flowchart TB
    ThreatSight -->|REST API| Splunk
    ThreatSight -->|Tickets| JIRA
    ThreatSight -->|Alerts| Slack
    ThreatSight -->|Reports| Compliance[Compliance Dashboard]
    ThreatSight -->|Data| Splunk

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Phase 5: AI-Enhanced Features

(Expected Completion: Month 12)

Advanced Capabilities

def predict_attack_paths(scan_results):
    """AI-powered attack path prediction"""
    return ml_model.predict(
        scan_results, 
        network_topology, 
        asset_criticality
    )

• Attack path simulation and prediction
• Zero-day vulnerability prediction
• Automated patch management guidance
• Natural language report generation
• Threat hunting query recommendations

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Testing Methodology

1. Unit Tests: pytest for all modules with >90% coverage
2. Integration Tests: Docker-compose environments with vulnerable containers
3. Test Targets:
   • Metasploitable, DVWA, and custom vulnerable applications
   • Industrial control system simulations (Modbus, BACnet)
   • IoT device emulations
4. Performance Benchmarks:
   • 1,000 hosts in <5 minutes (distributed scanning mode)
   • 100+ concurrent vulnerability assessments

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Legal & Compliance

• Ethical Use Policy: Requires signed authorization forms
• Data Handling: All scan results encrypted at rest (AES-256)
• Regulations: Compliant with GDPR Article 35 (DPIA), CCPA, HIPAA
• Responsible Disclosure: Built-in mechanisms for ethical reporting

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Contributing

1. Fork → Branch → Test → PR
2. Coding Standards:
   • Type hints for all functions
   • Google-style docstrings
   • Black-formatted code
   • Security-focused code reviews
3. Development Environment:
   • Pre-commit hooks for code quality
   • Dockerized development environment
   • Automated security scanning of code

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License

MIT License - See LICENSE.md for details.

Author: Parshant Kumar

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Resources

• Nmap Documentation
• NVD API Documentation
• EPSS API Documentation
• MITRE ATT&CK Framework
• CVSS v3.1 Specification

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Getting Started

# Clone the repository
git clone https://github.com/yourusername/threatsight.git
cd threatsight

# Set up virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

# Run basic scan
python threat_sight.py 192.168.1.0/24 --stealth --output report.html

For detailed documentation, see our Wiki.