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

<p>With the comprehensive promotion of “big data + energy”, new power network security threats are also more prominent, and the traditional security system mainly based on “protection” will face great challenges. Firstly, this paper proposes four kinds of network security situational awareness detection techniques based on distributed data analysis by combining the characteristics of big data in power networks. Secondly, the CRIT-LSTM power network security situational awareness model is constructed by improving its loss evaluation process using the cross entropy (CE) function and improving the LSTM unit structure using linear unit (ReLU). Finally, the performance of the three models is compared and analyzed under two aspects of neural network training and testing and various metrics to verify the models’ effectiveness. The results show that the improved CRIT-LSTM model based on deep learning, combining LSTM and ReLU algorithms, has an RMSE of 0.717 for the training set and 0.806 for the test set. 7.32% accuracy and 10.51% improvement in recall compared to the LSTM-only model. The power network security situational awareness model based on the CRIT-LSTM model proposed in this paper integrates various security system functions to maximize the defense against attacks and reduce unnecessary security risk losses.</p>
</abstract>

With the comprehensive promotion of “big data + energy”, new power network security threats are also more prominent, and the traditional security system mainly based on “protection” will face great challenges. Firstly, this paper proposes four kinds of network security situational awareness detection techniques based on distributed data analysis by combining the characteristics of big data in power networks. Secondly, the CRIT-LSTM power network security situational awareness model is constructed by improving its loss evaluation process using the cross entropy (CE) function and improving the LSTM unit structure using linear unit (ReLU). Finally, the performance of the three models is compared and analyzed under two aspects of neural network training and testing and various metrics to verify the models’ effectiveness. The results show that the improved CRIT-LSTM model based on deep learning, combining LSTM and ReLU algorithms, has an RMSE of 0.717 for the training set and 0.806 for the test set. 7.32% accuracy and 10.51% improvement in recall compared to the LSTM-only model. The power network security situational awareness model based on the CRIT-LSTM model proposed in this paper integrates various security system functions to maximize the defense against attacks and reduce unnecessary security risk losses.

Deep learning-based security situational awareness and detection technology for power networks in the context of big data

Applied Mathematics and Nonlinear Sciences

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

With the comprehensive promotion of “big data + energy”, new power network security threats are also more prominent, and the traditional security system mainly...

Deep learning-based security situational awareness and detection technology for power networks in the context of big data

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: 2939 - 2956

Eingereicht: 19. Jan. 2022

Akzeptiert: 04. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00012

Schlüsselwörter
Power network, Security situational awareness, CE function, ReLU algorithm, CRIT-LSTM model

© 2023 Xiaogang Gong et al., published by Sciendo

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

Deep learning-based security situational awareness and detection technology for power networks in the context of big data

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: 2939 - 2956

Eingereicht: 19. Jan. 2022

Akzeptiert: 04. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00012

SchlüsselwörterPower network, Security situational awareness, CE function, ReLU algorithm, CRIT-LSTM model

© 2023 Xiaogang Gong et al., published by Sciendo

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

Schlüsselwörter
Power network, Security situational awareness, CE function, ReLU algorithm, CRIT-LSTM model