NG-ICPS: Next Generation Industrial-CPS, Security Threats in the Era of Artificial Intelligence, and Open Challenges With Future Research Directions

Abstract

The complexity of next-generation industrial cyber-physical systems (NG-ICPSs) is increasing due to the integration of machine-embedded sensors, cyber-infrastructure, and physical processes, which calls for the new intelligent operation mechanisms to achieve system-level objectives. Although NG-ICPS has proliferated in many applications, such as advanced manufacturing, intelligent transportation, smart homes, etc., and achieved remarkable results. But these applications are susceptible to many problems and new security threats are some of them that goes beyond the scope of traditional communication and network security, due to the tight integration of cybers and physical systems. For redressal of this, several traditional authentication and data privacy schemes have been used in the recent past, but somehow, they did not satisfy the need for this emerging technology, due to their complex verification and validation processes. Recently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) enabled authentication and data preservation techniques had shown remarkable results to address the security problems of this technology at the system/client side and server side cost-effectively. Given that, in this article, we present a comprehensive survey of the current literature on NC-ICPS technology security threats and their countermeasures, with a focus on AI, ML, and DL-enabled techniques. We evaluate these techniques by identifying their advantages and disadvantages compared to traditional authentication and data preservation methods. In addition, we discussed the review articles published on this topic to acknowledge their contributions and limitations, because most of them cover a specific part of security concerns of this technology, and unable to present the true picture of all problems under one shallow. Building on this, we addressed the gaps in the literature by highlighting the open security challenges of NG-ICPS technology and suggesting potential future research directions, considering the capabilities of AI, ML, and DL-enabled algorithms. Finally, we compared this article sectionwise with rival review articles to claim its novelty followed by the question of reviewers, editors, students, and readers why this article is needed in the presence of these articles and what are its distinctive factor that makes this article different from them.