A novel method combining fuzzy SVM and sampling for imbalanced classification

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.