Efficient intrusion detection using multi-player generative adversarial networks (GANs): an ensemble-based deep learning architecture
Author:
Funder
Faculty of Engineering and Architectural Science, Ryerson University
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-08398-z.pdf
Reference75 articles.
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2. Alhajjar E, Maxwell P, Bastian N (2021) Adversarial machine learning in network intrusion detection systems. Expert Syst Appl 186:115782. https://doi.org/10.1016/j.eswa.2021.115782
3. Silva BR, Silveira RJ, da Neto MGS, Cortez PC, Gomes DG (2021) A comparative analysis of undersampling techniques for network intrusion detection systems design. J Commun Inf Syst. https://doi.org/10.14209/jcis.2021.3
4. Soleymanzadeh R, Kashef R (2022) The future roadmap for cyber-attack detection. In: 2022 6th international conference on cryptography, security and privacy (CSP), pp 66–70. https://doi.org/10.1109/CSP55486.2022.00021.
5. Ahmad Z, Shahid Khan A, Wai Shiang C, Abdullah J, Ahmad F (2021) Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol 32(1):4150. https://doi.org/10.1002/ett.4150
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