A Machine Learning–Based Smart Framework for Intrusion Detection in Cyber-Physical Systems
Hitakshi, Vandana Mohindru Sood, Kapil Mehta, Gurleen Kaur · 2024
Cyber-physical systems (CPS) are networked systems that can actively observe, assess, and respond to their surroundings. They are the outcome of a groundbreaking fusion of physical and digital qualities. CPSs are used in a variety of industries, including smart cities, healthcare, manufacturing, and energy. Embedded systems, sensors, actuators, and communication network technologies, as well as CPSs, are used to bridge the digital–physical gap. CPS enables intelligent decision-making, automation, and greater control over complex systems through the seamless integration of software, hardware, and physical processes. This chapter summarizes the investigation into the machine learning–based smart architecture for intrusion detection in CPS. Using machine learning methods, CPS can assess massive amounts of data from sensors, actuators, and other sources. This skill helps CPS to spot patterns, provide forecasts, and improve overall performance.