Enhanced Intrusion Detection Based Hybrid Meta-heuristic Feature Selection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70259-4_1
Reference31 articles.
1. Alkasassbeh, M., Al-Haj Baddar, S.: Intrusion detection systems: a state-of-the-art taxonomy and survey. Arab. J. Sci. Eng. 48(8), 10021–10064 (2023)
2. Rizvi, S., Scanlon, M., McGibney, J., Sheppard, J.: Deep learning based network intrusion detection system for resource-constrained environments. In: International Conference on Digital Forensics and Cyber Crime, pp. 355-367. Cham, Springer Nature Switzerland (2022). https://doi.org/10.1007/978-3-031-36574-4_21
3. Sarhan, M., Layeghy, S., Moustafa, N., Gallagher, M., Portmann, M.: Feature extraction for machine learning-based intrusion detection in IoT networks. Digital Commun. Netw. (2022)
4. Samunnisa, K., G. Sunil Vijaya Kumar, and K. Madhavi. "Intrusion detection system in distributed cloud computing: Hybrid clustering and classification methods." Measurement: Sensors 25 (2023)
5. Talukder, M.A., et al.: A dependable hybrid machine learning model for network intrusion detection. J. Inf. Secur. Appl. 72, 103405 (2023)
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