ML INTRUSION DETECTION
Decision tree-based intrusion detection system analyzing network traffic features, achieving 99.5% accuracy on large-scale datasets for real-time threat identification.
Technology Stack
Overview
This machine learning-based intrusion detection system uses decision tree algorithms to analyze network traffic patterns and identify potential security threats in real-time.
The system processes large-scale network datasets, extracting key features from network flows to distinguish between normal traffic and various types of network attacks including DDoS, port scans, and malware communications.
Achieved 99.5% accuracy through careful feature engineering, data preprocessing, and model optimization techniques, making it suitable for production network security environments.
Key Features
- •Real-time network traffic analysis and classification
- •99.5% accuracy on large-scale network datasets
- •Multi-class attack detection (DDoS, Port Scan, Malware)
- •Feature engineering for optimal model performance
- •Low false positive rate for production deployment
- •Scalable architecture for high-volume network monitoring
Performance Metrics
Technical Implementation
The system employs decision tree algorithms optimized for network security applications. Feature selection focuses on network flow characteristics, packet timing, and statistical anomalies. The model is trained on diverse attack scenarios and continuously updated to adapt to emerging threat patterns while maintaining high accuracy and low latency requirements.