ThreatSight: Enterprise Vulnerability Assessment Platform

From Nmap Wrapper to AI-Powered Threat Intelligence

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

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]

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

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

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

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

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

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

Testing Methodology

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

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

Contributing

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

License

MIT License - See LICENSE.md for details.

Author: Parshant Kumar

Resources

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.

FilesExpand file tree

README.md

Latest commit

History