<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Integrating industrial cyber-physical systems (ICPSs) with modern information technologies (5G, artificial intelligence, and big data analytics) has led to the development of industrial intelligence. Still, it has increased the vulnerability of such systems regarding cybersecurity. Traditional network intrusion detection methods for ICPSs are limited in identifying minority attack categories and suffer from high time complexity. To address these issues, this paper proposes a network intrusion detection scheme, which includes an information-theoretic hybrid feature selection method to reduce data dimensionality and the ALLKNN-LightGBM intrusion detection framework. Experimental results on three industrial datasets demonstrate that the proposed method outperforms four mainstream machine learning methods and other advanced intrusion detection techniques regarding accuracy, F-score, and run time complexity.</p>
</abstract>

Integrating industrial cyber-physical systems (ICPSs) with modern information technologies (5G, artificial intelligence, and big data analytics) has led to the development of industrial intelligence. Still, it has increased the vulnerability of such systems regarding cybersecurity. Traditional network intrusion detection methods for ICPSs are limited in identifying minority attack categories and suffer from high time complexity. To address these issues, this paper proposes a network intrusion detection scheme, which includes an information-theoretic hybrid feature selection method to reduce data dimensionality and the ALLKNN-LightGBM intrusion detection framework. Experimental results on three industrial datasets demonstrate that the proposed method outperforms four mainstream machine learning methods and other advanced intrusion detection techniques regarding accuracy, F-score, and run time complexity.

Fast Attack Detection Method for Imbalanced Data in Industrial Cyber-Physical Systems

Departmnt of Electrical and Computer Engineering

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Integrating industrial cyber-physical systems (ICPSs) with modern information technologies (5G, artificial intelligence, and big data analytics) has led to the...

Fast Attack Detection Method for Imbalanced Data in Industrial Cyber-Physical Systems

Online veröffentlicht: 30. Okt. 2023

Seitenbereich: 229 - 245

Eingereicht: 05. Mai 2023

Akzeptiert: 11. Sept. 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0017

Schlüsselwörter
intrusion detection system, industrial cyber-physical Systems, imbalanced data, all k-nearest neighbor, LightGBM.

© 2023 Meng Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fast Attack Detection Method for Imbalanced Data in Industrial Cyber-Physical Systems

Online veröffentlicht: 30. Okt. 2023

Seitenbereich: 229 - 245

Eingereicht: 05. Mai 2023

Akzeptiert: 11. Sept. 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0017

Schlüsselwörterintrusion detection system, industrial cyber-physical Systems, imbalanced data, all k-nearest neighbor, LightGBM.

© 2023 Meng Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
intrusion detection system, industrial cyber-physical Systems, imbalanced data, all k-nearest neighbor, LightGBM.