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Journal article
Using machine learning techniques to identify rare cyber-attacks on the UNSW-NB15 dataset
Security and privacy, Vol.2(6), e91
11/01/2019
Web of Science ID: WOS:000754969300003
Abstract
This paper uses a hybrid feature selection process and classification techniques to classify cyber-attacks in the UNSW-NB15 dataset. A combination of k-means clustering, and a correlation-based feature selection, were used to come up with an optimum subset of features and then two classification techniques, one probabilistic, Naive Bayes (NB), and a second, based on decision trees (J48), were employed. Our results show that this hybrid feature selection method in combination with the NB model was able to improve the classification accuracy of most attacks, especially the rare attacks. The false alarm rates were lower for most of the attacks, and particularly the rare attacks, with this combination of feature selection and the NB model. The J48 decision tree model, however, did not perform any better with the feature selection, but its classification rate for all attack families was already very high, with or without feature selection.
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Details
- Title
- Using machine learning techniques to identify rare cyber-attacks on the UNSW-NB15 dataset
- Publication Details
- Security and privacy, Vol.2(6), e91
- Resource Type
- Journal article
- Publisher
- Wiley
- Number of pages
- 13
- Grant note
- Askew Institute of the University of West Florida
- Copyright
- © 2019 John Wiley & Sons, Ltd.
- Identifiers
- WOS:000754969300003; 99380179095606600
- Academic Unit
- Computer Science; Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English