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Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey

Version 2 2024-06-06, 00:34
Version 1 2021-09-30, 07:54
journal contribution
posted on 2024-06-06, 00:34 authored by Jun ZhangJun Zhang, Lei PanLei Pan, QL Han, C Chen, S Wen, Y Xiang
With the booming of cyber attacks and cyber criminals against cyber-physical systems (CPSs), detecting these attacks remains challenging. It might be the worst of times, but it might be the best of times because of opportunities brought by machine learning (ML), in particular deep learning (DL). In general, DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data. DL models are adopted quickly to cyber attacks against CPS systems. In this survey, a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context. A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems. The methodology includes CPS scenario analysis, cyber attack identification, ML problem formulation, DL model customization, data acquisition for training, and performance evaluation. The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules. Moreover, excellent performance is achieved partly because of several high-quality datasets that are readily available for public use. Furthermore, challenges, opportunities, and research trends are pointed out for future research.

History

Journal

IEEE/CAA Journal of Automatica Sinica

Volume

9

Pagination

377-391

Location

Piscataway, NJ

ISSN

2329-9266

eISSN

2329-9274

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

3

Publisher

Institute of Electrical and Electronics Engineers