Feature selection for intrusion detection systems

Date
2020-12
Authors
Kamalov, Firuz
Moussa, Sherif
Zgheib, Rita
Mashaal, Omar
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
In this paper, we analyze existing feature selection methods to identify the key elements of network traffic data that allow intrusion detection. In addition, we propose a new feature selection method that addresses the challenge of considering continuous input features and discrete target values. We show that the proposed method performs well against the benchmark selection methods. We use our findings to develop a highly effective machine learning-based detection systems that achieves 99.9% accuracy in distinguishing between DDoS and benign signals. We believe that our results can be useful to experts who are interested in designing and building automated intrusion detection systems. ©2020 IEEE.
Description
This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (2020), available online at: https://doi.org/10.1109/ISCID51228.2020.00065
Keywords
Feature selection, intrusion detection system, Network security, Random forest, Computer crime, Intelligent computing, Intrusion detection, Benchmark selection, Continuous input, Detection system, Feature selection methods, Intrusion Detection Systems, Key elements, Network traffic, Target values, Feature extraction
Citation
Kamalov, F., Moussa, S., Zgheib, R., & Mashaal, O. (2020, December). Feature selection for intrusion detection systems. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 265-269). IEEE. https://doi.org/10.1109/ISCID51228.2020.00065