Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks

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Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks

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[1]ZHANG Zhen,ZHOU Yicheng,TIAN Hongpeng.Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):40-47.[doi:10.13705/j.issn.1671-6833.2024.06.001]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 40-47 Column: Public date: 2024-09-25

Title:: Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks

Author(s):: ZHANG Zhen¹; ZHOU Yicheng²; TIAN Hongpeng¹; 1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China

Keywords:: intrusion detection; anomaly detection; generative adversarial networks; image encoding; convolutionalneural networks

CLC:: TP393

DOI:: 10.13705/j.issn.1671-6833.2024.06.001

Abstract:: Address issues such as the inadequate consideration of inter-feature correlations in existing intrusion detection methods and the need for improved detection accuracy on high-dimensional discrete datasets, a network intrusion detection method MBGAN based on spatial features and generative adversarial networks was proposed. Initially, a transformation approach was devised to convert one-dimensional data into two-dimensional grayscale images, enabling convolutional kernels to capture richer contextual information. Subsequently, a bidirectional generative adversarial network model was employed for anomaly detection. The model was trained using network traffic images, incorporating the minimum Wasserstein distance and gradient penalty techniques to mitigate mode collapseand instability during generative adversarial network training. Experimental verification showed that the detection accuracy of the proposed method on the NSL-KDD, UNSW-NB15 and CICIDIS2017 datasets was 97. 4%, 92. 3% and94. 8%, the recall rates were 97. 2%, 93. 1% and 95. 6%, and the F1 were 97. 3%, 93. 0% and 95. 2%, respectively, which were better than those of other methods.

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