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Machine Learning Integration

Overview​

EyeNet incorporates machine learning capabilities to provide intelligent network monitoring and management features. This document details the ML components, their integration points, and how to work with them.

ML Components​

1. Anomaly Detection​

The anomaly detection system identifies unusual patterns in network traffic and system behavior.

interface AnomalyDetectionResult {
  isAnomaly: boolean;
  score: number;
  details: {
    metric: string;
    contribution: number;
  }[];
}

Features​

  • Real-time detection of network anomalies
  • Scoring system for anomaly severity
  • Detailed breakdown of contributing factors
  • Historical comparison baseline

2. Bandwidth Prediction​

Predicts future bandwidth usage based on historical patterns.

interface MLPrediction {
  timestamp: Date;
  value: number;
  confidence: number;
}

Model Architecture​

  • LSTM-based sequence model
  • 24-hour input window
  • 6-hour prediction horizon
  • Features: bandwidth usage, time of day, day of week

3. Traffic Classification​

Classifies network traffic into different categories for better management.

interface TrafficClassification {
  type: string;
  probability: number;
  features: {
    [key: string]: number;
  };
}

Categories​

  • Web traffic
  • Video streaming
  • File transfer
  • Real-time communication
  • Database operations

Integration Points​

1. Data Collection​

interface NetworkMetrics {
  timestamp: Date;
  bandwidth: {
    download: number;
    upload: number;
  };
  latency: number;
  packetLoss: number;
  // ... other metrics
}

2. Model Training​

interface TrainingConfig {
  epochs: number;
  batchSize: number;
  validationSplit: number;
  learningRate: number;
}

3. Inference​

interface InferenceRequest {
  modelType: 'anomaly' | 'bandwidth' | 'traffic';
  data: NetworkMetrics[];
  options?: {
    threshold?: number;
    window?: number;
  };
}

Model Management​

Training Process​

  1. Data collection and preprocessing
  2. Feature engineering
  3. Model training
  4. Validation
  5. Deployment

Model Versioning​

  • Version control for models
  • A/B testing capabilities
  • Rollback procedures
  • Performance monitoring

Performance Metrics​

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • RMSE for predictions

Development Guide​

Setting Up the Environment​

# Install dependencies
npm install @tensorflow/tfjs-node

# Optional GPU support
npm install @tensorflow/tfjs-node-gpu

Training a Model​

const model = new BandwidthPredictionModel();
await model.train(trainingData);
await model.saveModel('path/to/save');

Making Predictions​

const predictions = await model.predict(historicalData);
console.log(predictions);

Deployment​

Model Serving​

  • REST API endpoints
  • WebSocket for real-time inference
  • Model versioning
  • Load balancing

Monitoring​

  • Model performance metrics
  • Inference latency
  • Resource utilization
  • Error rates

Best Practices​

  1. Data Quality

    • Regular data validation
    • Handling missing values
    • Feature normalization
    • Outlier detection

  2. Model Management

    • Regular retraining
    • Version control
    • Performance monitoring
    • Fallback mechanisms

  3. Resource Optimization

    • Batch processing
    • Caching
    • Load balancing
    • Resource scaling