CoSen-IDS: A Novel Cost-Sensitive Intrusion Detection System on Imbalanced Data in 5G Networks
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5603-2_39
Reference25 articles.
1. Imanbayev, A., et al.: Research of machine learning algorithms for the development of intrusion detection systems in 5G mobile networks and beyond. Sensors 22, 9957 (2022). https://doi.org/10.3390/s22249957
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3. Li, X., Chen, W., Zhang, Q., Wu, L.: Building auto-encoder intrusion detection system based on random forest feature selection. Comput. Secur. 95, 101851 (2020). https://doi.org/10.1016/j.cose.2020.101851
4. Mirsky, Y., tshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089 (2018)
5. Zhang, H., Li, J.-L., Liu, X.-M., Dong, C.: Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection. Futur. Gener. Comput. Syst. 122, 130–143 (2021). https://doi.org/10.1016/j.future.2021.03.024
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