An Enhanced Crow Search Inspired Feature Selection Technique for Intrusion Detection Based Wireless Network System
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-021-08766-9.pdf
Reference29 articles.
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2. Hamed, T., Ernst, J. B., & Kremer, S. C. (2017). A survey and taxonomy on data and pre-processing techniques of intrusion detection systems. Computer and Network Security Essentials, 113–134.
3. Ashfaq, R. A. R., Wang, X.-Z., Huang, J. Z., Abbas, H., & He, Y.-L. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378, 484–497. https://doi.org/10.1016/j.ins.2016.04.019
4. DelaHoz, E., Ortiz, E. D. A., Ortega, J., & Prieto, B. (2015). PCA filtering and probabilistic SOM for network intrusion detection. Neurocomputing, 164, 71–81.
5. Aljawarneh, S., Aldwairi, M., & Yassein, M. B. (2018). Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, 152–160. https://doi.org/10.1016/j.jocs.2017.03.006
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