Ad hoc-based feature selection and support vector machine classifier for intrusion detection

2007 ◽  
Author(s):  
Xiao Haijun ◽  
Peng Fang ◽  
Wang Ling ◽  
Li Hongwei
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Support Vector Machine Classifier ◽  
Ad Hoc ◽  
Support Vector

Intrusion Detection System Based on Hybrid Feature Selection and Support Vector Machine (HFS-SVM)

2015 ◽  
Vol 781 ◽  
pp. 125-128 ◽  
Author(s):  
Yonchanok Khaokaew ◽  
Tanapat Anusas-Amornkul ◽  
Koonlachat Meesublak
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Motif Discovery ◽  
Detection System ◽  
Random Projection ◽  
Support Vector ◽  
Training Time ◽  
Detection Techniques ◽  
Projection Techniques

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.

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