An In-Depth Examination of Artificial Intelligence-Enhanced Cybersecurity in Robotics, Autonomous Systems, and Critical Infrastructures

Recent developments in cutting-edge robotics constantly face increased cyber threats, not only in terms of quantity and frequency of attacks, but also when it comes to quality and severity of the intrusions. This paper provides a systematic overview and critical assessment of state-of-the-art scientific developments in the security aspects of robotics, autonomous systems, and critical infrastructures. Our review highlights open research questions addressing significant research gaps and/or new conceptual frameworks given recent advancements in artificial intelligence (AI) and machine learning. Thus, the contributions of this paper can be summarized as follows. We first compare and contrast the benefits of multiple cutting-edge AI-based learning algorithms (e.g., fuzzy logic and neural networks) relative to traditional model-based systems (e.g., distributed control and filtering). Subsequently, we point out some specific benefits of AI algorithms to quickly learn and adapt the dynamics of non-linear systems in the absence of complex mathematical models. We also present some potential future research directions (open challenges) in the field. Lastly, this review also delivers an open message to encourage collaborations among experts from multiple disciplines. The implementation of multiple AI algorithms to tackle current security issues in robotics will transform and create novel hybrid knowledge for intelligent cybersecurity at the application level.