Intrusion Detection System Using Hybrid Machine Learning

The project introduces a state-of-the-art Intrusion Detection System (IDS) utilizing Hybrid Machine Learning that addresses the limitations of traditional systems by integrating anomaly and signature-based detection models. The proposed solution integrates anomaly detection and signature-based models for comprehensive detection of known and unknown attacks. Advanced machine learning algorithms ensure high accuracy while Explainable AI (XAI) provides interpretability for detection decisions. Federated learning is utilized to preserve data privacy. Designed for real-time detection in IOT and cloud environments, the system prioritizes scalability, accuracy, and interpretability, making it highly adaptable to modern network security needs.