Machine Learning for the Cybersecurity of Robotic Cyber-Physical Systems: A Review

摘要

The integration of cyber-physical systems (CPS) in various domains, such as transportation, healthcare, and manufacturing, has enabled efficient and reliable automation of processes. However, the increased connectivity and complexity of robotic cyber-physical systems have also made them vulnerable to various security threats. This paper explores the role of machine learning in enhancing the security of CPS with an emphasis on robotic systems. In this study, the analysis of the CPS architecture, protocols, and vulnerability trends is carried out. The past attacks on CPS devices, vulnerabilities, and threats associated with different components in CPS are also examined. Further, the use of machine learning techniques for intrusion detection, anomaly detection, and vulnerability assessment in robotic and general CPS is discussed. The challenges and limitations of applying machine learning to CPS security and identifying areas for future research are also investigated. The study highlights the potential application of machine learning in addressing security vulnerabilities in Cyber-Physical Systems (CPS) and points out the necessity for additional investigation to establish reliable and efficient machine learning-driven security solutions for CPS.