Securing Cyber Physical System Using Machine Learning: A Survey on Attack Resistant Algorithms.

In order to protect Cyber-Physical Systems (CPS) against constantly changing cyberattacks, machine learning (ML) algorithms must be integrated. The goal of this survey is to investigate attack-resistant machine learning methods that improve CPS security. The limits of standard techniques are emphasized while discussing notable issues in CPS security. The survey thoroughly explores a range of machine learning methods, such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Deep Neural Networks (DNN), that are utilized in CPS for behavior analysis, anomaly identification, and intrusion detection. We discuss the importance of having solid training data and the difficulties in ML model adaptation to the dynamic nature of CPS situations. We examine the trade-offs between responsiveness and precision as well as the effects of false positives and false negatives on attack detection. This papers aims to provide a quick overview of the strengths, limitations, and future prospects of these algorithms, enabling stakeholders to formulate effective strategies for CPS security. [ABSTRACT FROM AUTHOR]