Computational Intelligence Techniques for Cyberspace Intrusion Detection System
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-05752-6_9
Reference33 articles.
1. S.U. Otor, B.O. Akinyemi, T.A. Aladesanmi, G.A. Aderounmu, An improved bio-inspired based intrusion detection model for cyberspace. Cogent Eng. 8(1), 1859667 (2021). https://doi.org/10.1080/23311916.2020.1859667
2. S. Dilek, H. Çakır, M. Aydın, Applications of artificial intelligence techniques to combating cyber crimes: a review. https://doi.org/10.5121/ijaia.2015.6102
3. X.K. Li, W. Chen, Q. Zhang, L., Wu, Building auto-encoder intrusion detection system based on random forest feature selection. Comput. Secur. (2020). https://doi.org/10.1016/j.cose.2020.101851
4. Y. Meng, L.F. Kwok, Enhancing false alarm reduction using voted ensemble selection in intrusion detection. Int. J. Comput. Intell. Syst. 6(4), 626–638 (2013). https://doi.org/10.1080/18756891.2013.802114
5. E. Min, J. Long, Q. Liu, J. Cui, W. Chen, TR-IDS: anomaly-based intrusion detection through text-convolutional neural network and random forest. Secur. Commun. Netw. 9 (2018) (Article ID 4943509). https://doi.org/10.1155/2018/4943509
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