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<p>To improve the efficiency and accuracy of network intrusion discrimination, this paper introduces intrusion detection techniques in a generative adversarial network model. Firstly, a basic framework of a generative adversarial network is constructed. Secondly, the generative adversarial network is trained, and the training process is analyzed to find the data discrimination point in the network. Finally, ELM (Extreme Learning) algorithm is introduced at this discriminating point. The output weight matrix is derived using the minimization square loss function and least squares regression to improve the intrusion discrimination accuracy and intrusion cracking rate in the generative adversarial network, improving network security. To verify the security of the ELM algorithm, this paper simulates the intrusion of the constructed network model, and the results show that the intrusion detection accuracy of the generative adversarial network model based on the ELM algorithm can reach 100%, which is higher than that of DCGAN network 19% and LSGAN network 23%, respectively. The intrusion cracking rate of its layer 5 neural network can reach 92% at the second 2.5 seconds of the simulated intrusion. From the above results, it is clear that the generative adversarial network model based on the ELM algorithm can accurately detect and efficiently crack the intrusion to improve the network security performance.</p>
</abstract>

To improve the efficiency and accuracy of network intrusion discrimination, this paper introduces intrusion detection techniques in a generative adversarial network model. Firstly, a basic framework of a generative adversarial network is constructed. Secondly, the generative adversarial network is trained, and the training process is analyzed to find the data discrimination point in the network. Finally, ELM (Extreme Learning) algorithm is introduced at this discriminating point. The output weight matrix is derived using the minimization square loss function and least squares regression to improve the intrusion discrimination accuracy and intrusion cracking rate in the generative adversarial network, improving network security. To verify the security of the ELM algorithm, this paper simulates the intrusion of the constructed network model, and the results show that the intrusion detection accuracy of the generative adversarial network model based on the ELM algorithm can reach 100%, which is higher than that of DCGAN network 19% and LSGAN network 23%, respectively. The intrusion cracking rate of its layer 5 neural network can reach 92% at the second 2.5 seconds of the simulated intrusion. From the above results, it is clear that the generative adversarial network model based on the ELM algorithm can accurately detect and efficiently crack the intrusion to improve the network security performance.

Application of ELM algorithm-based generative adversarial network model in network security

Applied Mathematics and Nonlinear Sciences

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

To improve the efficiency and accuracy of network intrusion discrimination, this paper introduces intrusion detection techniques in a generative adversarial...

Application of ELM algorithm-based generative adversarial network model in network security

Publié en ligne: 19 juil. 2023

Pages: -

Reçu: 16 avr. 2022

Accepté: 18 nov. 2022

DOI: https://doi.org/10.2478/amns.2023.2.00057

Mots clés
Network security, ELM algorithm, Generative adversarial networks, Minimax squared loss function, Intrusion discrimination

© 2023 Zhixian Wen, published by Sciendo

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

Application of ELM algorithm-based generative adversarial network model in network security

Publié en ligne: 19 juil. 2023

Pages: -

Reçu: 16 avr. 2022

Accepté: 18 nov. 2022

DOI: https://doi.org/10.2478/amns.2023.2.00057

Mots clésNetwork security, ELM algorithm, Generative adversarial networks, Minimax squared loss function, Intrusion discrimination

© 2023 Zhixian Wen, published by Sciendo

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

Mots clés
Network security, ELM algorithm, Generative adversarial networks, Minimax squared loss function, Intrusion discrimination