Application of ELM algorithm-based generative adversarial network model in network security
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 t...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns.2023.2.00057 |
| _version_ | 1797340841912565760 |
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| author | Wen Zhixian |
| author_facet | Wen Zhixian |
| author_sort | Wen Zhixian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:10:11Z |
| format | Article |
| id | doaj.art-dc472d06b8d945abac818bdd6bff9f5b |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:10:11Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-dc472d06b8d945abac818bdd6bff9f5b2024-01-29T08:52:28ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00057Application of ELM algorithm-based generative adversarial network model in network securityWen Zhixian01School of Electronic Information and Electrical Engineering, Tianshui Normal University, Tianshui, Gansu, 741001, China.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.https://doi.org/10.2478/amns.2023.2.00057network securityelm algorithmgenerative adversarial networksminimax squared loss functionintrusion discrimination68m01 |
| spellingShingle | Wen Zhixian Application of ELM algorithm-based generative adversarial network model in network security Applied Mathematics and Nonlinear Sciences network security elm algorithm generative adversarial networks minimax squared loss function intrusion discrimination 68m01 |
| title | Application of ELM algorithm-based generative adversarial network model in network security |
| title_full | Application of ELM algorithm-based generative adversarial network model in network security |
| title_fullStr | Application of ELM algorithm-based generative adversarial network model in network security |
| title_full_unstemmed | Application of ELM algorithm-based generative adversarial network model in network security |
| title_short | Application of ELM algorithm-based generative adversarial network model in network security |
| title_sort | application of elm algorithm based generative adversarial network model in network security |
| topic | network security elm algorithm generative adversarial networks minimax squared loss function intrusion discrimination 68m01 |
| url | https://doi.org/10.2478/amns.2023.2.00057 |
| work_keys_str_mv | AT wenzhixian applicationofelmalgorithmbasedgenerativeadversarialnetworkmodelinnetworksecurity |