Intrusion Detection System Based on Hybrid Feature Selection and Support Vector Machine (HFS-SVM)
2015 ◽
Vol 781
◽
pp. 125-128
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Keyword(s):
In recent years, anomaly based intrusion detection techniques are continuously developed and a support vector machine (SVM) is one of the technique. However, it requires training time and storage if there are lots of numbers of features. In this paper, a hybrid feature selection, using Correlation based on Feature Selection and Motif Discovery using Random Projection techniques, is proposed to reduce the number of features from 41 to 3 features with KDD'99 dataset. It is compared with a regular SVM technique with 41 features. The results show that the accuracy rate is also high at 98% and the training time is less than the regular SVM almost by half.
2013 ◽
Vol 655-657
◽
pp. 1787-1790
2018 ◽
Vol 7
(2.7)
◽
pp. 277