Deep Learning-Based Intrusion Detection Methods in Cyber-Physical Systems: Challenges and Future Trends

Abstract

A cyber-physical system (CPS) integrates various interconnected physical processes, computing resources, and networking units, as well as monitors the process and applications of the computing systems. Interconnection of the physical and cyber world initiates threatening security challenges, especially with the increasing complexity of communication networks. Despite efforts to combat these challenges, it is difficult to detect and analyze cyber-physical attacks in a complex CPS. Machine learning-based models have been adopted by researchers to analyze cyber-physical security systems. This paper discusses the security threats, vulnerabilities, challenges, and attacks of CPS. Initially, the CPS architecture is presented as a layered approach including the physical layer, network layer, and application layer in terms of functionality. Then, different cyber-physical attacks regarding each layer are elaborated, in addition to challenges and key issues associated with each layer. Afterward, deep learning models are analyzed for malicious URLs and intrusion detection in cyber-physical systems. A multilayer perceptron architecture is utilized for experiments using the malicious URL detection dataset and KDD Cup99 dataset, and its performance is compared with existing works. Lastly, we provide a roadmap of future research directions for cyber-physical security to investigate attacks concerning their source, complexity, and impact.