When Federated Learning Meets Game Theory: A Cooperative Framework to Secure IIoT Applications on Edge Computing

Industry 5.0 is rapidly growing as the next industrial evolution, aiming to improve production efficiency in the 21
^st
century. This evolution relies mainly on advanced digital technologies, including Industrial Internet of Things (IIoT), by deploying multiple IIoT devices within industrial systems. Such a setup increases the possibility of threats, especially with the emergence of IIoT botnets. This can provide attackers with more sophisticated tools to conduct devastating IIoT attacks. Besides, machine learning (ML) and deep learning (DL) are considered as powerful techniques to efficiently detect IIoT attacks. However, the centralized way in building learning models and the lack of up-to-date datasets that contain the main attacks are still ongoing challenges. In this context, multiaccess edge computing (MEC) and federated learning (FL) are two promising complementary technologies. MEC brings computing capabilities at the edge of the industrial systems, while FL leverages the edge resources to enable a privacy-aware collaborative learning, especially in multiindustrial systems context. In this article, we design a novel MEC-based framework to secure IIoT applications leveraging FL, called FedGame. Specifically, FedGame enables multiple MEC domains to collaborate securely to deal with an IIoT attack, while preserving the privacy of IIoT devices. Moreover, a noncooperative game is formulated on the top of FedGame, to enable MEC nodes acquiring the needed virtual resources from the centralized MEC orchestrator, to deal with each type of IIoT attacks. We evaluate FedGame using real-world IIoT attacks; the experimental results show not only the accuracy of FedGame against centralized ML/DL schemes while preserving the privacy of Industrial systems but also its efficiency in providing required MECs resources and, thus, dealing with IIoT attacks.