Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Jindaluang, Wattana; *
Affiliations: Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
Correspondence: [*] Corresponding author. Wattana Jindaluang, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand. Tel.: +66 53 9434 1216; E-mail: wjindaluang@gmail.com.
Abstract: A class imbalance problem is a problem in which the number of majority class and minority class varies greatly. In this article, we propose an oversampling method using GA and k-Nearest Neighbors (kNN) to deal with a network intrusion, a class imbalance problem. We use GA as the main algorithm and use a kNN as its fitness function. We compare the proposed method with a very popular oversampling technique which is a SMOTE family. The experimental results show that the proposed method provides better Accuracy, Precision, and F-measure values than a SMOTE family in almost all datasets with almost all classifiers. Moreover, in some datasets with some classifiers, the proposed method also gives a better Recall value than a SMOTE family as well. This is because the proposed method can generate new intruders in a more independent area than a SMOTE family.
Keywords: Oversampling, class imbalanced problem, genetic algorithm, k-nearest neighbors
DOI: 10.3233/JIFS-213430
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2515-2528, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl