O-RAN Security Test Case Generator 🛡️

Advanced AI-Powered O-RAN Network Security Analysis Platform

Comprehensive security test case generation and threat analysis for Open Radio Access Networks

🚀 Quick Start • 📊 Features • 🛠️ Installation • 📖 Documentation • 🤝 Contributing

🎯 Overview

The O-RAN Security Test Case Generator is a cutting-edge AI-powered platform designed to revolutionize security testing and threat analysis for Open Radio Access Networks (O-RAN). This comprehensive tool combines advanced machine learning, deep learning, and artificial intelligence techniques to provide automated security analysis and comprehensive network optimization.pen RAN Security Test Case Generator

A comprehensive O-RAN security analysis tool that generates security test cases using AI and provides quantitative dataset analysis from O-RAN free sources.

✅ COMPLETED FEATURES

🔧 Core Migration

• ✅ Complete Gemini 2.5 Flash Migration: Replaced all OpenAI API usage with Google Gemini
• ✅ Environment Setup: Updated .env and .env.template for Gemini-only operation
• ✅ Dependencies: Updated Pipfile with all required data analysis libraries
• ✅ Testing: Comprehensive test suite for all components

🔍 O-RAN Dataset Analysis & Quantitative Assessment

• ✅ Multi-Dataset Support: Analyze 10+ O-RAN datasets simultaneously

  • Performance metrics (Near-RT RIC, E2 traces, xApp performance)
  • Security metrics (vulnerability assessments, threat analysis)
  • Configuration data (O-RAN components, RIC configuration)
  • Standards compliance (ASVS, CAPEC, CWE)

• ✅ Security Analysis:

  • Vulnerability assessment with CVSS scoring
  • Threat pattern analysis and categorization
  • Security score calculation (0-10 scale)
  • Component-level security analysis

• ✅ Performance Analysis:

  • Latency, throughput, and availability metrics
  • Resource utilization tracking (CPU, memory, network)
  • SLA compliance monitoring
  • Performance trend analysis

• ✅ Interactive Visualizations:

  • Security dashboards with gauge charts
  • Performance trend graphs
  • Vulnerability distribution charts
  • Component analysis visualizations

📊 O-RAN Free Source Data Collection

• ✅ O-RAN Alliance Specifications: Automated collection of official O-RAN specs
• ✅ 3GPP Standards: Integration with 3GPP technical specifications
• ✅ Cybersecurity Frameworks: NIST, MITRE ATT&CK, CWE, CAPEC integration
• ✅ Export Capabilities: JSON and CSV format support
• ✅ Metadata Tracking: Complete audit trail of collected data

🎯 Enhanced User Interface

• ✅ Multi-Page Navigation: Seamless switching between:
  • Security Test Case Generator
  • O-RAN Dataset Analysis
  • Data Collection Interface
• ✅ Interactive Controls: Real-time analysis configuration
• ✅ Progress Monitoring: Live updates during data collection and analysis
• ✅ Results Export: One-click export of analysis results

🧪 Comprehensive Testing

• ✅ Integration Testing: End-to-end system verification
• ✅ Component Testing: Individual module validation
• ✅ API Testing: Gemini integration verification
• ✅ Data Validation: Dataset integrity checking

🚀 GETTING STARTED

Prerequisites

• Python 3.8+
• Gemini API key from Google AI Studio

Quick Setup

# Clone repository
git clone https://github.com/sachin-deshik-10/O-RAN-Security-Test-Case-Generator.git
cd O-RAN-Security-Test-Case-Generator

# Install dependencies
pip install -r requirements.txt
# OR use pipenv
pipenv install && pipenv shell

# Set up environment
cp .env.template .env
# Edit .env and add your GEMINI_API_KEY

# Run comprehensive tests
python test_integration.py

# Launch application
streamlit run app.py

📈 USAGE EXAMPLES

Security Test Case Generation

1. Navigate to the main page
2. Input your O-RAN use case scenario
3. Select relevant attack patterns (CAPEC, CWE)
4. Choose security requirements (ASVS)
5. Generate AI-powered test cases

Dataset Analysis

1. Navigate to "O-RAN Dataset Analysis"
2. Select datasets: Performance, Security, E2 Traces, etc.
3. Configure analysis options:
   • Basic Statistics ✓
   • Security Metrics ✓
   • Performance Metrics ✓
   • Interactive Visualizations ✓
4. View comprehensive analysis results

Data Collection

1. Navigate to "Data Collection"
2. Select sources: O-RAN Alliance, 3GPP, NIST, etc.
3. Configure collection parameters
4. Start automated data collection
5. Export results in JSON/CSV format

📊 QUANTITATIVE RESULTS

Sample Analysis Output

{
  "security_analysis": {
    "total_vulnerabilities": 50,
    "critical_issues": 12,
    "security_score": 4.4,
    "cvss_distribution": {
      "critical": 12,
      "high": 18,
      "medium": 15,
      "low": 5
    }
  },
  "performance_analysis": {
    "avg_latency": 4.8,
    "throughput": 1050.5,
    "availability": 99.7,
    "resource_utilization": {
      "cpu": 68.5,
      "memory": 72.1,
      "network": 45.3
    }
  }
}

Collected Data Sources

• O-RAN Alliance: 4 specifications (E2AP, A1AP, O1, xApp SDK)
• 3GPP Standards: 15+ relevant technical specifications
• NIST Framework: 108 cybersecurity controls
• MITRE ATT&CK: 185 ICS-specific attack techniques
• CWE Database: 900+ weakness patterns
• CAPEC: 544 attack patterns

🔧 TESTING

Run All Tests

# Comprehensive integration test
python test_integration.py

# Individual component tests
python test_gemini.py           # Gemini API integration
python test_data_analysis.py    # Data analysis functionality
python test_data_collection.py  # Data collection functionality
python test_comprehensive.py    # Additional comprehensive tests

Test Results

• ✅ 8/8 Integration Tests Passed (100% success rate)
• ✅ All imports successful
• ✅ Data files validated
• ✅ Gemini API integration working
• ✅ NLTK data available
• ✅ Analysis functionality verified
• ✅ Data collection operational

🎯 WHAT'S NEXT: RECOMMENDED ENHANCEMENTS

1. Advanced Machine Learning Integration

• Anomaly Detection: ML-based detection of unusual O-RAN behavior patterns
• Predictive Analytics: Forecast security threats and performance issues
• Automated Classification: AI-powered categorization of vulnerabilities and threats
• Real-time Monitoring: Continuous analysis of live O-RAN data streams

2. Enhanced Data Sources

• O-RAN SC Integration: Connect to O-RAN Software Community live repositories
• Real-time Telemetry: Integration with actual O-RAN deployments
• Vendor-specific Data: Support for major O-RAN vendor datasets
• 5G Core Integration: Extend analysis to 5G Core network components

3. Advanced Analytics & Reporting

• Compliance Dashboards: Automated compliance reporting for O-RAN standards
• Risk Assessment: Comprehensive risk scoring and mitigation recommendations
• Executive Reports: High-level security and performance summaries
• Trend Analysis: Historical analysis and forecasting capabilities

4. Integration & Automation

• API Endpoints: RESTful API for programmatic access to analysis capabilities
• CI/CD Integration: Automated security testing in O-RAN development pipelines
• SIEM Integration: Export findings to security information and event management systems
• Ticketing System: Automated issue creation for identified vulnerabilities

5. User Experience Enhancements

• Role-based Access: Different interfaces for developers, security analysts, and executives
• Custom Dashboards: User-configurable analysis dashboards
• Notification System: Real-time alerts for critical security issues
• Mobile Interface: Responsive design for mobile access

6. Cloud & Scalability

• Azure Integration: Deploy on Azure with auto-scaling capabilities
• Multi-tenant Support: Support for multiple O-RAN deployments
• Big Data Processing: Handle large-scale O-RAN datasets efficiently
• Global Deployment: Multi-region deployment for global O-RAN analysis

7. Advanced Security Features

• Threat Intelligence: Integration with commercial threat intelligence feeds
• Attack Simulation: Automated attack scenario generation and testing
• Incident Response: Automated response recommendations for security incidents
• Forensic Analysis: Deep-dive analysis capabilities for security investigations

🏗️ ARCHITECTURE OVERVIEW

Current Implementation

O-RAN Security Test Case Generator
├── Core Application (app.py)
│   ├── Security Test Case Generation
│   ├── Dataset Analysis Interface
│   └── Data Collection Interface
├── Data Analysis Engine (oran_data_analyzer.py)
│   ├── Statistical Analysis
│   ├── Security Metrics
│   ├── Performance Metrics
│   └── Visualization Generation
├── Data Collection Engine (collect_oran_data.py)
│   ├── O-RAN Alliance Specs
│   ├── 3GPP Standards
│   ├── Cybersecurity Frameworks
│   └── Export Capabilities
├── Testing Framework
│   ├── Integration Tests
│   ├── Component Tests
│   └── API Tests
└── Data Repository (./data/)
    ├── O-RAN Components
    ├── Security Standards
    ├── Performance Data
    └── Compliance Frameworks

Recommended Future Architecture

Cloud-Native O-RAN Security Platform
├── Frontend (React/Vue.js)
│   ├── Executive Dashboard
│   ├── Analyst Interface
│   └── Developer Console
├── API Gateway
│   ├── Authentication
│   ├── Rate Limiting
│   └── Request Routing
├── Microservices
│   ├── Data Collection Service
│   ├── Analysis Engine
│   ├── ML/AI Service
│   └── Notification Service
├── Data Layer
│   ├── Time-series Database
│   ├── Graph Database
│   └── Document Store
└── Integration Layer
    ├── O-RAN SC Connector
    ├── Cloud APIs
    └── SIEM Integrations

🤝 CONTRIBUTING

Adding New Data Sources

1. Extend ORANDataCollector class with new collection methods
2. Add data source configuration to the UI
3. Implement data validation and transformation
4. Add comprehensive tests

Adding New Analysis Metrics

1. Extend ORANDataAnalyzer class with new analysis methods
2. Add visualization components using Plotly
3. Update the Streamlit interface
4. Add unit tests and integration tests

Adding New Visualizations

1. Create new Plotly chart components
2. Integrate with the analysis dashboard
3. Add export functionality
4. Test across different datasets

📄 LICENSE

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 ACKNOWLEDGMENTS

• O-RAN Alliance for open specifications
• 3GPP for technical standards
• NIST for cybersecurity framework
• MITRE Corporation for ATT&CK framework
• Google for Gemini AI API
• Streamlit for the web framework