Experience-Driven Attack Design and Federated-Learning-Based Intrusion Detection in Industry 4.0

Abstract

The advent of Industry 4.0 facilitates the Int- ernet-of-Things-based-transactive energy system (IoTES), which enables innovative services with numerous independent distributed systems. These systems generate heterogeneous data in bulk, which become susceptible to cyber-attacks, particularly the stealthy false data injection attacks (FDIAs). The existing centralized FDIA detection algorithms often breach data privacy and fail to perform effectively in highly dynamic and distributed environments, such as IoTES. To resolve the issue, initially, a recurrent deep deterministic policy gradient is utilized to invent an experience-driven FDIA in a complex IoTES. The attacker intends to intelligently exploit the data integrity of smart energy meters with insufficient knowledge of the system. Subsequently, to countermove the stealth and enable independent clients to train a centralized model while keeping each client’s data privacy intact, a deep-federated-learning-based decentralized FDIA detection method using an attentive aggregation is exploited in this article. The proposed approach is capable of parallel computing and can reliably identify the stealthy FDIA on all the nodes simultaneously. Simulation results validate that the proposed scheme outperforms the state-of-the-art methods under a distributed environment with a significantly higher detection accuracy and lower computational complexity while keeping the data privacy intact.