Title: Intrusion Detection System Based on Hybrid Feature Selection and Support Vector Machine (HFS-SVM)
Abstract: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 t...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.Read More
Publication Year: 2015
Publication Date: 2015-08-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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