Abstract
Intrusion detection systems use datasets with various features to detect attacks and protect computers and network systems from these attacks. However, some of these features are irrelevant and may reduce the intrusion detection system’s speed and accuracy. In this study, we use feature selection methods to eliminate non-relevant features. We compare the performance of fourteen feature-selection methods, on three ML techniques using the UNSW-NB15, Kyoto 2006+ and DoHBrw-2020 datasets. The most relevant features of each dataset are identified, which show that feature selection methods can increase the accuracy of anomaly detection and classification.
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Avila, R., Khoury, R., Pere, C., Khanmohammadi, K. (2022). Employing Feature Selection to Improve the Performance of Intrusion Detection Systems. In: Aïmeur, E., Laurent, M., Yaich, R., Dupont, B., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2021. Lecture Notes in Computer Science, vol 13291. Springer, Cham. https://doi.org/10.1007/978-3-031-08147-7_7
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