Secure Data Offloading and Resource Allocation Against Hybrid Intrusions for IIoT: A Fully Decentralized Framework

Edge computing is fundamental to filling the various quality-of-service needs for Industrial Internet of Things (IIoT) applications. However, introducing edge computing to IIoT inevitably results in hybrid intrusion problems and fails to satisfy the security demands of IIoT. Fortunately, Lagrange coded computing has emerged as a low-complexity and low-overhead solution for resisting hybrid intrusions during data offloading and processing. However, how to make decentralized, accurate, and real-time encoding/offloading/decoding decisions remains challenging. This article designs a fully decentralized training and decision-making framework to address the joint secure data offloading and resource allocation problem against hybrid intrusions for dynamic and uncertain IIoT, attempting to minimize the long-run energy and delay costs while improving the data confidentiality, integrity, and availability. It is proposed a fully decentralized multiagent actor–critic-based secure data offloading (FM-SDO) algorithm to solve the secure data offloading subproblem, wherein each industrial end device utilizes its local information to learn and execute its policy independently. This algorithm improves the structure of actor and critic networks and designs a multiagent alternant updating mechanism to increase learning accuracy, convergence, and stability. Based on the received offloading decisions of each device, each edge server leverages the Lagrange multiplier approach and Karush–Kuhn–Tucker condition to make fast and decentralized resource allocation decisions. Finally, we employed an IIoT intelligent production line platform named iCandyBox to test the performance of the FM-SDO algorithm. Experiment results suggest that the FM-SDO algorithm effectively reduces the total energy and delay costs while increasing the capability of resisting hybrid intrusions.