Stacked Denoised Auto-encoding Network-based Kernel Principal Component Analysis for Cyber Physical Systems Intrusion Detection in Business Management

Zhihao Song1

  1. School of Business Administration, Zhengzhou University of Science and Technology
    Zhengzhou 450064 China
    zsshanln@163.com

Abstract

At present, the network data under the environment of industrial information physical system is larger and more complex. Traditionally, feature extraction by machine learning is cumbersome and computation-intensive, which is not conducive to anomaly detection of industrial network data. To solve the above problems, this paper proposes a stacked denoised auto-encoding network based on kernel principal component analysis for industrial cyber physical systems intrusion detection. Firstly, a novel kernel principal component analysis method is used to reduce the data feature dimension and obtain a new low-dimension feature data set. Then, a multi-stacked denoised auto-encoding network model is used to classify and identify the data after dimensionality reduction by voting. Experimental results show that the proposed method has better classification performance and detection efficiency by comparing the state-of-the-art intrusion detection methods.

Key words

industrial cyber physical systems, intrusion detection, stacked denoised auto-encoding network, kernel principal component analysis

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS240314055S

Publication information

Volume 21, Issue 4 (September 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Song, Z.: Stacked Denoised Auto-encoding Network-based Kernel Principal Component Analysis for Cyber Physical Systems Intrusion Detection in Business Management. Computer Science and Information Systems, Vol. 21, No. 4, 1725–1743. (2024), https://doi.org/10.2298/CSIS240314055S