Secure Authentication and Trust Management Scheme for Edge AI-Enabled Cyber-Physical Systems

Cyber-physical systems (CPSs) connected in the form of the Industrial Internet of Things (IIoT) are susceptible to various security threats. Due to the extensive deployment of infrastructure for IIoT devices, the trustworthiness and security of data are among the major concerns in CPSs. Therefore, establishing security measures against potential threats through trust assessment and trust authentication has become a key goal. Blockchain has the characteristics of traceability, anonymity, transparency, etc., and can achieve trust authentication for trust assessment. In our work, we propose a lightweight decentralized authentication and trust management scheme for edge AI-enabled CPSs that supports access control on the basis of extended chaotic maps, which meets the privacy and security needs of data transmission in a broader sense. Moreover, we develop a trust model for checking the trustworthiness of data collected by smart devices/sensor nodes. A formal security analysis is executed by utilizing the broadly applicable real-or-random (RoR) model. Our scheme, which is different from previous methods, combines high security with relatively low communication and computational costs. Through an informal security analysis, we verify that our proposal is in compliance with the security requirements and can withstand various forms of attacks. Furthermore, the functionality and performance analysis results indicate that our method is better suited for lightweight validation in CPS networks while providing a higher level of security than other methods.