Open Access

ARTICLE

A New Intrusion Detection Algorithm AE-3WD for Industrial Control Network

Yongzhong Li^1,2,*, Cong Li¹, Yuheng Li³, Shipeng Zhang²

1 School of Computer Science and Engineering, Taizhou Institute of Sci. and Tec. NJUST, Taizhou, 225300, China
2 School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
3 Suzhou Institute of Technology, Jiangsu University of Science and Technology, Suzhou, 215600, China

* Corresponding Author: Yongzhong Li. Email:

Journal of New Media 2022, 4(4), 205-217. https://doi.org/10.32604/jnm.2022.034778

Received 01 January 2022; Accepted 01 January 2022; Issue published 12 December 2022

Abstract

In this paper, we propose a intrusion detection algorithm based on auto-encoder and three-way decisions (AE-3WD) for industrial control networks, aiming at the security problem of industrial control network. The ideology of deep learning is similar to the idea of intrusion detection. Deep learning is a kind of intelligent algorithm and has the ability of automatically learning. It uses self-learning to enhance the experience and dynamic classification capabilities. We use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning, a denoising AutoEncoder and three-way decisions intrusion detection method AE-3WD is proposed to improve intrusion detection accuracy. In the processing, deep learning AutoEncoder is used to extract the features of high-dimensional data by combining the coefficient penalty and reconstruction loss function of the encode layer during the training mode. A multi-feature space can be constructed by multiple feature extractions from AutoEncoder, and then a decision for intrusion behavior or normal behavior is made by three-way decisions. NSL-KDD data sets are used to the experiments. The experiment results prove that our proposed method can extract meaningful features and effectively improve the performance of intrusion detection.

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Keywords

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