<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Addressing the complex equipment and network challenges in new energy power plant industrial control systems, this study introduces a Markov time-varying machine learning algorithm, leveraging classification-constrained Boltzmann machines for real-time network security risk prediction. By employing a hybrid training mode for innovative feature extraction and classification, the algorithm forecasts future security risk states through an up-to-date state transition probability matrix. Integrating with the Markov time-varying model enhances the efficiency over traditional Boltzmann machines, facilitating nuanced network state analyses. The proposed model demonstrates high effectiveness against various network attacks, with average precision, recall, and F1 scores of 0.96, 0.93, and 0.94, respectively, and maintains over 80% accuracy under noise levels up to 40 dB. This research provides a solid foundation for proactive security defense mechanisms in industrial control systems.</p>
</abstract>

Addressing the complex equipment and network challenges in new energy power plant industrial control systems, this study introduces a Markov time-varying machine learning algorithm, leveraging classification-constrained Boltzmann machines for real-time network security risk prediction. By employing a hybrid training mode for innovative feature extraction and classification, the algorithm forecasts future security risk states through an up-to-date state transition probability matrix. Integrating with the Markov time-varying model enhances the efficiency over traditional Boltzmann machines, facilitating nuanced network state analyses. The proposed model demonstrates high effectiveness against various network attacks, with average precision, recall, and F1 scores of 0.96, 0.93, and 0.94, respectively, and maintains over 80% accuracy under noise levels up to 40 dB. This research provides a solid foundation for proactive security defense mechanisms in industrial control systems.

Construction of Cybersecurity and Risk Prediction Model for New Energy Power Plants under Machine Learning Algorithm

Information and Communication Department China Electrical Power Research Institute Department

Applied Mathematics and Nonlinear Sciences

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

Addressing the complex equipment and network challenges in new energy power plant industrial control systems, this study introduces a Markov time-varying...

Construction of Cybersecurity and Risk Prediction Model for New Energy Power Plants under Machine Learning Algorithm

Publié en ligne: 03 mai 2024

Pages: -

Reçu: 11 avr. 2024

Accepté: 17 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-0889

Mots clés
Restricted Boltzmann machine, Markov time-varying, Hybrid training, Network security, Risk prediction

© 2024 Xiaofu Sun et al., published by Sciendo

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

Construction of Cybersecurity and Risk Prediction Model for New Energy Power Plants under Machine Learning Algorithm

Publié en ligne: 03 mai 2024

Pages: -

Reçu: 11 avr. 2024

Accepté: 17 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-0889

Mots clésRestricted Boltzmann machine, Markov time-varying, Hybrid training, Network security, Risk prediction

© 2024 Xiaofu Sun et al., published by Sciendo

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

Mots clés
Restricted Boltzmann machine, Markov time-varying, Hybrid training, Network security, Risk prediction