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<p>Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.</p>
</abstract>

Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.

Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance

Applied Mathematics and Nonlinear Sciences

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

Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional...

Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance

Online veröffentlicht: 01. Apr. 2024

Seitenbereich: -

Eingereicht: 24. Jan. 2024

Akzeptiert: 02. Feb. 2024

DOI: https://doi.org/10.2478/amns-2024-0714

Schlüsselwörter
Substation Network Security, Situational Awareness, Remote Operation and Maintenance, Linear Discriminant Analysis

© 2024 Jing Bai et al., published by Sciendo

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

Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance

Online veröffentlicht: 01. Apr. 2024

Seitenbereich: -

Eingereicht: 24. Jan. 2024

Akzeptiert: 02. Feb. 2024

DOI: https://doi.org/10.2478/amns-2024-0714

SchlüsselwörterSubstation Network Security, Situational Awareness, Remote Operation and Maintenance, Linear Discriminant Analysis

© 2024 Jing Bai et al., published by Sciendo

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

Schlüsselwörter
Substation Network Security, Situational Awareness, Remote Operation and Maintenance, Linear Discriminant Analysis