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

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 paper, we propose a novel federated deep learning scheme, named DeepFed, to detect cyber threats against softwarized industrial CPSs. Specifically, we first design a new deep learning based intrusion detection model for industrial CPSs, by making use of a convolutional neur