Optimized CNN-BiGRU Intrusion Detection Model with SMOTE Enhancement: Using Optuna for Automated Hyperparameter Tuning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65018-5_7
Reference16 articles.
1. Li, Y., Zhang, B.: An intrusion detection algorithm based on deep CNN. Comput. Appl. Softw. 37(4), 324–328 (2020)
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3. ElSayed, M.S., Le-Khac, N.-A., Albahar, M.A., Jurcut, A.: A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique. J. Netw. Comput. Appl. 191, 103160 (2021). https://doi.org/10.1016/j.jnca.2021.103160
4. Di Mauro, M., Galatro, G., Liotta, A.: Experimental review of neural-based approaches for network intrusion management. IEEE Trans. Netw. Serv. Manage. 17(4), 2480–2495 (2020). https://doi.org/10.1109/TNSM.2020.3024225
5. Hao, S., Long, J., Yang, Y.: BL-IDS: detecting web attacks using Bi-LSTM model based on deep learning. In: Li, J., Liu, Z., Peng, H. (eds.) Security and Privacy in New Computing Environments. SPNCE 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 284, pp. 551–563. Springer, Cham (2019). 10.1007/978-3-030-21373-2_45
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