Cybersecurity enhancement using conditional generative adversarial network with transformer-based conditional variational autoencoder
Since, Artificial Intelligence is highly developing and concatenating into several domains, cybersecurity is an important field of delivering both the advantages and disadvantages. In addition to this, Artificial Intelligence is applied in a wide variety of applications like healthcare sector, conte...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 161; s. 112220 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
12.12.2025
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| Témata: | |
| ISSN: | 0952-1976 |
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| Abstract | Since, Artificial Intelligence is highly developing and concatenating into several domains, cybersecurity is an important field of delivering both the advantages and disadvantages. In addition to this, Artificial Intelligence is applied in a wide variety of applications like healthcare sector, content creation and entertainment and financial industries. Therefore, this work finds the efficiency of Artificial Intelligence -oriented cybersecurity metrics in succeeding the digital environment over elevating cyber threats. Here, the developed models consist of two different stages while implementing the model. In the first stage, the essential dataset is assembled from the benchmark data source. These datasets are assembled by using Generative Artificial Intelligence (Gen Artificial Intelligence networks). Consequently, the raw data is given as an input to Conditional Hybrid Network for cybersecurity enhancement. Further, the Transformer-based Conditional Variational Autoencoders with Spatial-temporal Attention are designed for feature extraction that is subjected to the Conditional Generative Adversarial Network for classifying the cyber attacks. Henceforth, the developed network is evaluated and designed with multiple measures. Comparing baseline models, the suggested network obtains higher performance for developing security over cyber networks. |
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| AbstractList | Since, Artificial Intelligence is highly developing and concatenating into several domains, cybersecurity is an important field of delivering both the advantages and disadvantages. In addition to this, Artificial Intelligence is applied in a wide variety of applications like healthcare sector, content creation and entertainment and financial industries. Therefore, this work finds the efficiency of Artificial Intelligence -oriented cybersecurity metrics in succeeding the digital environment over elevating cyber threats. Here, the developed models consist of two different stages while implementing the model. In the first stage, the essential dataset is assembled from the benchmark data source. These datasets are assembled by using Generative Artificial Intelligence (Gen Artificial Intelligence networks). Consequently, the raw data is given as an input to Conditional Hybrid Network for cybersecurity enhancement. Further, the Transformer-based Conditional Variational Autoencoders with Spatial-temporal Attention are designed for feature extraction that is subjected to the Conditional Generative Adversarial Network for classifying the cyber attacks. Henceforth, the developed network is evaluated and designed with multiple measures. Comparing baseline models, the suggested network obtains higher performance for developing security over cyber networks. |
| ArticleNumber | 112220 |
| Author | Singh, Gurupdesh Singh, Sandeep Singh, Prithvipal Singh, Amritpal |
| Author_xml | – sequence: 1 givenname: Prithvipal surname: Singh fullname: Singh, Prithvipal email: prithvipalsingh89@gmail.com organization: Department of Computer Science, Guru Nanak Dev University (GNDU), Amritsar, Punjab, 143005, India – sequence: 2 givenname: Sandeep surname: Singh fullname: Singh, Sandeep email: sandeep_feat@sgtuniversity.org organization: Department of Computer Science and Engineering, Faculty of Engineering and Technology, SGT University, Gurugram, 122505, Haryana, India – sequence: 3 givenname: Gurupdesh surname: Singh fullname: Singh, Gurupdesh email: gurupdeshdhillon@gmail.com organization: Department of Computer Science, Guru Nanak Dev University (GNDU), Amritsar, Punjab, 143005, India – sequence: 4 givenname: Amritpal surname: Singh fullname: Singh, Amritpal email: amritsinghmand@gmail.com organization: Pacific Rim School District, BC, Canada |
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| Cites_doi | 10.1016/j.dss.2023.114102 10.1016/j.jnca.2024.103926 10.3389/fnbot.2022.925210 10.1007/s11277-022-09776-x 10.1016/j.jksus.2023.102713 10.58496/MJCS/2023/009 10.1016/j.autcon.2022.104412 10.1057/s42984-020-00007-w 10.1016/j.engappai.2023.105971 10.1109/ACCESS.2020.2968045 10.1007/s10586-024-04355-0 10.1109/ACCESS.2019.2921555 10.1007/s11042-024-18169-0 10.1109/TTS.2023.3280109 10.1016/j.epsr.2022.108603 10.1007/s10462-024-10827-x 10.1109/ACCESS.2022.3185748 10.1016/j.csi.2023.103802 10.1109/TIM.2023.3305655 10.1177/14759217211053776 10.3390/electronics13173393 10.1145/3469379.3469386 10.1109/ACCESS.2024.3465045 10.1007/s10922-022-09659-3 |
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| Keywords | Conditional hybrid network Transformer-based conditional variational AutoEncoder Conditional generative adversarial network Generative artificial intelligence Spatial-temporal attention mechanism Cybersecurity enhancement |
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| SubjectTerms | Conditional generative adversarial network Conditional hybrid network Cybersecurity enhancement Generative artificial intelligence Spatial-temporal attention mechanism Transformer-based conditional variational AutoEncoder |
| Title | Cybersecurity enhancement using conditional generative adversarial network with transformer-based conditional variational autoencoder |
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