Iterative per Group Feature Selection for Intrusion Detection
Author | Chkirbene Z. |
Author | Erbad A. |
Author | Hamila R. |
Author | Gouissem A. |
Author | Mohamed A. |
Author | Guizani M. |
Author | Hamdi M. |
Available date | 2022-04-21T08:58:25Z |
Publication Date | 2020 |
Publication Name | 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/IWCMC48107.2020.9148067 |
Abstract | Network security is an critical subject in any distributed network. Recently, machine learning has proven their efficiency for intrusion detection. By using a comprehensive dataset with multiple attack types, a well-trained model can be created to improve the anomaly detection performance. However, high dimensional data sets are a significant challenge for machine learning. In fact, learning algorithms considering all features in the input data, may cause over-fitting to irrelevant aspects of the data and increase the computational time caused by the process of similar features that provide redundant information, which is a critical problem especially for users with constrained resources. In this paper, we propose a new and efficient feature selection technique for intrusion detection in modern networks called Iterative Per Group Feature Selection (IPGFS). IPGFS reduces the number of features in the input data and selects the best features using the performance accuracy of the classifier. The features are sorted and selected according to their accuracy score. Both the UNSW and NSLKDD datasets are used in this paper to validate the proposed model and verify its efficiency in detecting intrusions. The simulation results show that the proposed model can reduce the number of features for the two dataset while successfully detecting intrusions with better accuracy compared to state-of-the-art techniques. Index Cloud security, feature selection, accuracy, machine learning techniques. 2020 IEEE. |
Sponsor | Qatar National Research Fund |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Anomaly detection Clustering algorithms Efficiency Input output programs Intrusion detection Iterative methods Learning algorithms Machine learning Mobile computing Network security Computational time Constrained resources Detection performance Distributed networks Efficient feature selections High dimensional data Machine learning techniques State-of-the-art techniques Feature extraction |
Type | Conference Paper |
Pagination | 708-713 |
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Computer Science & Engineering [2284 items ]