Article: A novel method combining fuzzy SVM and sampling for imbalanced classification Journal: International Journal of Applied Systemic Studies (IJASS) 2018 Vol.8 No.1 pp.1 - 31 Abstract: The class imbalance problem has been reported to reduce performance of many existing learning algorithms in intrusion detection. However, the detection rates for minority classes still need to be improved. Thus, the novel hybrid method FSVMs is proposed to solve the problem in the paper, which integrates the prevailing sampling method SMOTE with fuzzy semi-supervised SVM learning approach to class imbalanced intrusion detection data. The basic KDD Cup 1999 dataset, NSLKDD dataset and imbalanced dataset from UCI are used to evaluate the performance of proposed model. Experiment results show that the proposed method outperforms other state-of-the-art classifiers including support vector machine (SVM), back propagation neural network (BPNN), Bayes, k-nearest neighbour (KNN), decision tree (DT), random forest (RF) and four sampling methods in the aspects of detection rate and false alarm rate, and has better robustness for imbalanced classification. Inderscience Publishers - linking academia, business and industry through research

Title: A novel method combining fuzzy SVM and sampling for imbalanced classification

Authors: Tao Ma; Ying Hou; Jian-Jun Cheng; Xiao-Yun Chen

Addresses: Department of Information Science and Engineering, Lanzhou University, LanZhou 730000, China ' Department of Information Science and Engineering, Lanzhou University, LanZhou 730000, China ' Department of Information Science and Engineering, Lanzhou University, LanZhou 730000, China; Gansu Resources and Environmental Science Data Engineering Technology Research Center, Lanzhou 730000, China ' Department of Information Science and Engineering, Lanzhou University, LanZhou 730000, China

Abstract: The class imbalance problem has been reported to reduce performance of many existing learning algorithms in intrusion detection. However, the detection rates for minority classes still need to be improved. Thus, the novel hybrid method FSVMs is proposed to solve the problem in the paper, which integrates the prevailing sampling method SMOTE with fuzzy semi-supervised SVM learning approach to class imbalanced intrusion detection data. The basic KDD Cup 1999 dataset, NSLKDD dataset and imbalanced dataset from UCI are used to evaluate the performance of proposed model. Experiment results show that the proposed method outperforms other state-of-the-art classifiers including support vector machine (SVM), back propagation neural network (BPNN), Bayes, k-nearest neighbour (KNN), decision tree (DT), random forest (RF) and four sampling methods in the aspects of detection rate and false alarm rate, and has better robustness for imbalanced classification.

Keywords: intrusion detection; fuzziness; support vector machine; SVM; SMOTE; semi-supervised learning; SSL; imbalance classification; applied systemic studies.

DOI: 10.1504/IJASS.2018.091844

International Journal of Applied Systemic Studies, 2018 Vol.8 No.1, pp.1 - 31

Accepted: 12 Jan 2018
Published online: 18 May 2018 *

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Title: A novel method combining fuzzy SVM and sampling for imbalanced classification

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