Network Anomaly Detection System

Enterprise-Grade Network Anomaly Detection System
Leveraging Machine Learning & OMNeT++ for Advanced Network Security

🎯 Overview

A cutting-edge network anomaly detection system that combines advanced machine learning algorithms with OMNeT++ simulation capabilities. Our system focuses on robust data collection and analysis, with a planned evolution towards real-time detection and dynamic adaptation capabilities.

System Architecture

graph TD
    A[Data Collection Layer] --> B[Preprocessing Engine]
    B --> C[Feature Extraction]
    C --> D[ML Pipeline]
    D --> E[Anomaly Detection]
    
    subgraph "Data Processing"
        B --> F[Data Cleaning]
        F --> G[Feature Engineering]
        G --> H[Data Validation]
    end
    
    subgraph "ML Components"
        D --> I[Model Training]
        I --> J[Model Validation]
        J --> K[Model Deployment]
    end

🌟 Key Features & Capabilities

Feature	Current Status	Future Goal	Implementation Details
Data Collection	✅ Operational	Enhanced real-time capabilities	Custom packet capture, PCAP analysis, Traffic sampling
ML Processing	✅ Offline Analysis	Real-time processing	TensorFlow-based models, Scikit-learn pipelines
OMNeT++ Integration	🔄 Basic Setup	Full real-time integration	Custom modules, Network scenarios
Adaptation	📊 Static Models	Dynamic network-agnostic models	Transfer learning, Online adaptation
Visualization	📈 Basic Metrics	Interactive real-time dashboards	Grafana integration, Custom dashboards

🚀 Development Status

Current Implementation

✅ Data Collection Framework
- Network packet capture system
- Protocol-specific preprocessing
- Advanced feature extraction
- Data validation pipeline
✅ ML Model Implementation
- Supervised learning models
- Anomaly detection algorithms
- Model validation framework
- Performance monitoring
✅ OMNeT++ Foundation
- Network topology modeling
- Traffic pattern simulation
- Basic integration framework
- Custom module development

Future Roadmap (2024)

🎯 Enhanced Accuracy (Q1)
- Deep learning integration
- Feature selection optimization
- Ensemble model implementation
- Cross-validation framework
🔄 Dynamic Adaptation (Q2)
- Network-agnostic detection
- Real-time model updates
- Transfer learning capabilities
- Adaptive thresholding
⚡ Real-time Integration (Q3)
- OMNeT++ real-time processing
- Live traffic analysis
- Interactive visualization
- Performance optimization

Development Phases

Phase	Status	Deliverable	Weight	Documentation
Research Proposal	✅	Proposal Document	10%	View
Simulation Tools Study	✅	Technical Report	10%	View
Network Design	✅	Design Document	20%	View
Simulation Experiments	✅	Results Report	20%	View
Research Article	✅	IEEE Paper	20%	View
Final Presentation	✅	Presentation	20%	View

💡 Technical Implementation

Dynamic Adaptation Framework

class DynamicNetworkDetector:
    """
    Advanced network anomaly detection with dynamic adaptation capabilities.
    Supports real-time model updates and network-agnostic detection.
    """
    def __init__(self, config: Dict[str, Any]):
        self.base_model = self._initialize_model(config)
        self.network_profiles: Dict[str, NetworkProfile] = {}
        self.adaptation_metrics: List[AdaptationMetric] = []
        self.feature_extractors: Dict[str, FeatureExtractor] = {}

    async def adapt_to_network(self, network_type: str) -> bool:
        """
        Dynamically adjust model parameters based on network characteristics.
        
        Args:
            network_type: Type of network to adapt to
            
        Returns:
            bool: Success status of adaptation
        """
        try:
            profile = self.network_profiles.get(network_type)
            if not profile:
                profile = await self._create_network_profile(network_type)
            
            return await self._adapt_model_parameters(profile)
        except AdaptationError as e:
            logger.error(f"Adaptation failed: {e}")
            return False

    async def update_model_realtime(self, new_data: NetworkData) -> ModelUpdateResult:
        """
        Update model in real-time with streaming network data.
        
        Args:
            new_data: New network data for model update
            
        Returns:
            ModelUpdateResult: Results of model update
        """
        validation_result = await self._validate_data(new_data)
        if validation_result.is_valid:
            return await self._update_model(new_data)
        return ModelUpdateResult(success=False, error=validation_result.error)

OMNeT++ Integration

/**
 * Real-time anomaly detection module for OMNeT++
 * Supports live traffic analysis and model adaptation
 */
class RealTimeAnomalyDetector : public cSimpleModule {
protected:
    virtual void initialize() override;
    virtual void handleMessage(cMessage *msg) override;
    
private:
    // Core components
    std::unique_ptr<MLModel> anomalyModel;
    std::unique_ptr<DataProcessor> streamProcessor;
    
    // Configuration
    DetectorConfig config;
    
    // Performance monitoring
    Statistics stats;
    
    // Helper methods
    void processPacket(Packet *packet);
    void updateModel(const DataBatch& batch);
    void reportAnomaly(const AnomalyEvent& event);
};

📊 System Performance

Current Metrics

Metric	Performance	Details
Model Accuracy	95%	Tested on standard network datasets
False Positive Rate	<1%	With current threshold settings
Data Processing	Offline	Batch processing of network data
Analysis Latency	~500ms	Average processing time per batch
Supported Protocols	TCP/IP, UDP	Basic protocol analysis

Target Specifications

Metric	Goal	Timeline
Real-time Accuracy	>98%	Q2 2024
Processing Latency	<100ms	Q3 2024
Adaptation Time	<1s	Q2 2024
Network Coverage	Universal	Q4 2024
Protocol Support	All major	Q3 2024

Research Team

Batu Burgu	Javad Ibrahimli	Kerem Karadeniz
ID: 0402010098	ID: 040210932	ID: 0402110049

javadibrahimli/Anomaly-Detection-in-Networks-Traffic-Scenarios-Using-ML-Based-Algorithms