Unknown Security Attack Detection of Industrial Control System by Deep Learning

Jie Wang, Pengfei Li (), Weiqiang Kong and Ran An
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Jie Wang: School of Software Technology, Dalian University of Technology, Dalian 116620, China
Pengfei Li: School of Software Technology, Dalian University of Technology, Dalian 116620, China
Weiqiang Kong: School of Software Technology, Dalian University of Technology, Dalian 116620, China
Ran An: School of Software Technology, Dalian University of Technology, Dalian 116620, China

Mathematics, 2022, vol. 10, issue 16, 1-17

Abstract: With the rapid development of network technologies, the network security of industrial control systems has aroused widespread concern. As a defense mechanism, an ideal intrusion detection system (IDS) can effectively detect abnormal behaviors in a system without affecting the performance of the industrial control system (ICS). Many deep learning methods are used to build an IDS, which rely on massive numbers of variously labeled samples for model training. However, network traffic is imbalanced, and it is difficult for researchers to obtain sufficient attack samples. In addition, the attack variants are rich, and constructing all possible attack types in advance is impossible. In order to overcome these challenges and improve the performance of an IDS, this paper presents a novel intrusion detection approach which integrates a one-dimensional convolutional autoencoder (1DCAE) and support vector data description (SVDD) for the first time. For the two-stage training process, 1DCAE fails to retain the key features of intrusion detection and SVDD has to add restrictions, so a joint optimization solution is introduced. A three-stage optimization process is proposed to obtain better performance. Experiments on the benchmark intrusion detection dataset NSL-KDD show that the proposed method can effectively detect various unknown attacks, learning with only normal traffic. Compared with the recent state-of-art intrusion detection baselines, the proposed method is improved in most metrics.

Keywords: network security; network intrusion detection; deep learning; auto-encoder; SVDD (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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