An Intelligent Intrusion Detection System for Integrated Edge Computing CPS with Machine Learning

The integration of Edge Computing and Cyber-Physical Systems (CPS) has revolutionized various domains, offering real-time processing capabilities closer to data sources.However, this paradigm shift brings significant security challenges, necessitating advanced intrusion detection systems.This project introduces an intelligent intrusion detection system that leverages the power of Machine Learning.By seamlessly integrating machine learning algorithms, anomaly detection techniques, and edge computing, this system fortifies CPS against potential threats.The system operates with real-time responsiveness, providing robust protection while maintaining the efficiency required by CPS.Through the utilization of advanced data analytics and predictive models, it can identify and mitigate intrusions, anomalies, and security breaches.This research underscores the crucial role of Machine Learning in bolstering security in integrated Edge Computing CPS, contributing to a safer and more reliable future for these critical systems.