DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
Sichuan University · University of New Brunswick
Abstract
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…
Citation impact
- FWCI
- 42.42
- Percentile
- 100%
- References
- 24
Authors
6Topics & keywords
- Computer science
- Deep learning
- Intrusion detection system
- Cyber-physical system
- Computer security
- Artificial intelligence
- Paillier cryptosystem
- Industrial control system
- Industry, innovation and infrastructure