An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set

Abstract

Information security is extremely critical issues for every organization to protect information from unauthorized access. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviours. Basically Intrusion detection system is a classifier that can classify the data as normal or attacks. In this research paper, we have proposed ANN-Bayesian Net-GR technique that means ensemble of Artificial Neural Network (ANN) and Bayesian Net with Gain Ratio (GR) feature selection technique. We have applied various individual classification techniques and its ensemble model on KDD99 and NSL-KDD data set to check the robustness of model. Due to irrelevant features in data set, also applied Gain Ratio feature selection technique on best model. Finally our proposed model produces highest accuracy compare to others.