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IEEE/ICACT20220329 Slide.11        [Big Slide]	Oral Presentation
	We use recursive feature addition algorithm on NSL-KDD for feature selection. After feature selection, the One-Class SVM is trained according to the selected features. The Table 6 shows the results of One-Class SVM tested on NSL-KDD with all features and the optimal subset of features. Next, we compare the training and detection time of the One-Class SVM model before and after feature selection, as shown in Table 7. Therefore, according to the detection results of the NSL-KDD dataset, the recursive feature addition algorithm is effective.

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