Research Area:  Machine Learning
The rapid convergence of legacy industrial infrastructures with intelligent networking and computing technologies (e.g., 5G, software-defined networking, and artificial intelligence), have dramatically increased the attack surface of industrial cyber-physical systems (CPSs). However, withstanding cyber threats to such large-scale, complex, and heterogeneous industrial CPSs has been extremely challenging, due to the insufficiency of high-quality attack examples. In this article, we propose a novel federated deep learning scheme, named DeepFed, to detect cyber threats against industrial CPSs. Specifically, we first design a new deep learning-based intrusion detection model for industrial CPSs, by making use of a convolutional neural network and a gated recurrent unit. Second, we develop a federated learning framework, allowing multiple industrial CPSs to collectively build a comprehensive intrusion detection model in a privacy-preserving way. Further, a Paillier cryptosystem-based secure communication protocol is crafted to preserve the security and privacy of model parameters through the training process. Extensive experiments on a real industrial CPS dataset demonstrate the high effectiveness of the proposed DeepFed scheme in detecting various types of cyber threats to industrial CPSs and the superiorities over state-of-the-art schemes.
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Author(s) Name:  Beibei Li; Yuhao Wu; Jiarui Song; Rongxing Lu; Tao Li; Liang Zhao
Journal name:  IEEE Transactions on Industrial Informatics
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Publisher name:  IEEE
DOI:  10.1109/TII.2020.3023430
Volume Information:  ( Volume: 17, Issue: 8, Aug. 2021) Page(s): 5615 - 5624
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9195012