Effective Analysis of Feature Selection Algorithms for Network based Intrusion Detection System

Abstract

Malicious activities can harm the security of the system. These activities must be avoided. Network traffic data can be monitored and analyzed by using intrusion detection system. Different data mining classification techniques are used to detect network attacks. Dimensionality reduction performs key role in the Intrusion Detection System, since detecting anomalies is time-consuming. Recently a lot of work has been done in feature selection. But, most of the authors have modified the KDD99 test dataset. Modification of training dataset is valid but modifying test dataset is against the machine learning ethics. This work comprises some of the recently proposed feature selection algorithm such as Information gain, Gain Ratio and Correlation-based feature selection with the objective of determining the reduced feature set. The performance is evaluated using a combination of any two feature selection technique. This study proposes a new heuristic based feature selection algorithm using naive Bayes classifier to detect the important reduced feature set. The results are evaluated on c4.5 decision tree classifier and the results are compared with the existing works. The evaluated results show that the proposed reduced feature set gives the effective and efficient performance.