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

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IEEE Transactions on Industrial Informatics


Industry 5.0 is growing as the next industrial evolution, aiming to improve production efficiency in the 21st century. This evolution relies mainly on advanced digital technologies, including Industrial Internet of Things (IIoT), by deploying multiple and heterogenous 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. In this paper, we design a novel MEC-based framework to secure IIoT applications leveraging federated learning, called FedGame. Specifically, FedGame enables multiple MEC domains to securely collaborate in order to cope with IIoT attacks, while preserving the privacy of IIoT devices. Moreover, a non-cooperative game is formulated on top of FedGame, to enable MEC nodes acquiring the needed virtual resources from the centralized MEC orchestrator, and hence to deal with each type of IIoT attacks. We evaluate FedGame using real-world IIoT attacks; 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 the required MECs resources. IEEE

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Computation theory, Computer games, Data privacy, Digital devices, Edge computing, Efficiency, Internet of things, Centralised, Collaborative Work, Cooperative frameworks, Edge computing, Federated learning, Game, Industrial systems, Noncooperative game, Privacy, Security threats, Game theory


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