DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems

作者：Beibei Li, Yuhao Wu, Jiarui Song, Rongxing Lu, Tao Li, Liang Zhao · 发表于：IEEE Transactions on Industrial Informatics · 年份：2020 · DOI：10.1109/tii.2020.3023430 · 被引用次数：594 · 研究领域：Smart Grid Security and Resilience、Network Security and Intrusion Detection、Internet Traffic Analysis and Secure E-voting

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.