An evaluation of feature selection and reduction algorithms for network IDS data

Abstract

Intrusion detection is concerned with monitoring and analysing events occurring in a computer system in order to discover potential malicious activity. Data mining, which is part of the procedure of knowledge discovery in databases, is the process of analysing the collected data to find patterns or correlations. As the amount of data collected, store and processed only increases, so does the significance and importance of intrusion detection and data mining. A dataset that has been particularly exposed to research is the dataset used for the Third International Knowledge Discovery and Data Mining Tools competition, KDD99. The KDD99 dataset has been used to identify what data mining techniques relate to certain attack and employed to demonstrate that decision trees are more efficient than the Naïve Bayes model when it comes to detecting new attacks. When it comes to detecting network intrusions, the C4.5 algorithm performs better than SVM. The aim of our research is to evaluate and compare the usage of various feature selection and reduction algorithms against publicly available datasets. In this contribution, the focus is on feature selection and reduction algorithms. Three feature selection algorithms, consisting of an attribute evaluator and a test method, have been used. Initial results indicate that the performance of the classifier is unaffected by reducing the number of attributes.