A Comparative Analysis on Ensemble Learning and Deep Learning Based Intrusion Detection Systems over the NCC2 Dataset
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56599-1_16
Reference9 articles.
1. M.Y. Alzahrani, A.M. Bamhdi, Hybrid deep-learning model to detect botnet attacks over internet of things environments. Soft Comput. 26(16), 7721–7735 (2022)
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3. T. Hasan, J. Malik, I. Bibi, W.U. Khan, F.N. Al-Wesabi, K. Dev, G. Huang, Securing industrial internet of things against botnet attacks using hybrid deep learning approach. IEEE Trans. Netw. Sci. Eng. (2022)
4. Y. Yin, J. Jang-Jaccard, W. Xu, A. Singh, J. Zhu, F. Sabrina, J. Kwak, IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset. J. Big Data 10(1), 1–26 (2023)
5. G. Mohiuddin, Z. Lin, J. Zheng, J. Wu, W. Li, Y. Fang, S. Wang, J. Chen, X. Zeng, Intrusion detection using hybridized meta-heuristic techniques with Weighted XGBoost Classifier. Expert Syst. Appl. 232, 120596 (2023)
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