Advancements in Anomaly Detection: A Review of Machine Learning Applications in Cyber-Physical System Networks

Abstract

In the practical world, Cyber-Physical Systems have integrated physical systems and software management in the cyber-world, with networks responsible for information interchange. CPSs are key technologies for various industrial domains, including intelligent medical systems, transport systems, and smart grids. The advancements in cybersecurity have surpassed the rapid growth of CPS, with new security challenges and threat models that lack an integrated and cohesive framework. The review methodology includes the search strategy along with the inclusion and exclusion criteria of fifteen studies conducted in the past ten years. The studies specific to the relevant topic have been added, while the others have been excluded. According to the results, Machine Learning (ML) algorithms and systems can synthesize data. It is employed in cyber-physical security to alleviate concerns regarding the safety and reliability of the findings. ML offers a solution to complex problems, enhancing computer-human interaction and enabling problem-solving in areas where custom-built algorithms are impractical. A comprehensive overview of the application of ML across various domains, such as smart grids, smart vehicles, healthcare systems, and environmental monitoring, has been included. However, a few challenges are associated with implementing ML techniques in CPS networks, including feature selection complexity, model performance, deployment challenges, algorithm biases, model mismatches, and the need to foster a robust safety culture. Overall, integrating ML techniques with CPS networks holds promise for enhancing system safety, reliability, and security but requires ongoing refinement and adaptation to address existing limitations and emerging threats.