Generalization of Deep Learning for Cyber-Physical System Security: A Survey

Abstract

Cyber-Physical Systems (CPSs)have become ubiquitous in recent years and has become the core of modern critical infrastructure and industrial applications. Therefore, ensuring security is a prime concern. Due to the success of Deep Learning (DL)in a multitude of domains, development of DL based CPS security applications have received increased interest in the past few years. Developing generalized models is critical since the models have to perform well under threats that they havent trained on. However, despite the broad body of work on using DL for ensuring the security of CPSs, to our best knowledge very little work exists where the focus is on the generalization capabilities of these DL applications. In this paper, we intend to provide a concise survey of the regularization methods for DL algorithms used in security-related applications in CPSs and thus could be used to improve the generalization capability of DL based cyber-physical system based security applications. Further, we provide a brief insight into the current challenges and future directions as well.