Title :
Fast Feature Reduction in intrusion detection datasets
Author :
Parsazad, Shafigh ; Saboori, Ehsan ; Allahyar, Amin
Author_Institution :
Dept. Of Comput. Eng., Ferdowsi Univ. of Mashhad, Mashhad, Iran
Abstract :
In the most intrusion detection systems (IDS), a system tries to learn characteristics of different type of attacks by analyzing packets that sent or received in network. These packets have a lot of features. But not all of them is required to be analyzed to detect that specific type of attack. Detection speed and computational cost is another vital matter here, because in these types of problems, datasets are very huge regularly. In this paper we tried to propose a very simple and fast feature selection method to eliminate features with no helpful information on them. Result faster learning in process of redundant feature omission. We compared our proposed method with three most successful similarity based feature selection algorithm including Correlation Coefficient, Least Square Regression Error and Maximal Information Compression Index. After that we used recommended features by each of these algorithms in two popular classifiers including: Bayes and KNN classifier to measure the quality of the recommendations. Experimental result shows that although the proposed method can´t outperform evaluated algorithms with high differences in accuracy, but in computational cost it has huge superiority over them.
Keywords :
Bayes methods; data compression; feature extraction; learning (artificial intelligence); least squares approximations; pattern classification; regression analysis; security of data; Bayes classifier; KNN classifier; attack characteristic learning; attack detection; computational cost; correlation coefficient; detection speed; fast feature reduction; fast feature selection method; intrusion detection dataset; intrusion detection system; least square regression error; maximal information compression index; packet analysis; redundant feature omission; similarity based feature selection algorithm; Accuracy; Classification algorithms; Correlation; Feature extraction; Intrusion detection; Probes; KDD 99; computational cost; fast feature selection; intrusion detection;
Conference_Titel :
MIPRO, 2012 Proceedings of the 35th International Convention
Conference_Location :
Opatija
Print_ISBN :
978-1-4673-2577-6