A novel approach for selective feature mechanism for two-phase intrusion detection system

Abstract

Intrusion Detection is an important aspect to secure the computing systems from different intrusions. To improve the accuracy and to reduce the computational time, this paper proposes a two-phase hybrid method based on the SVM and RNN. In addition, this paper also had a proposal to obtain a few sets of features with a feature selection technique in which the detection performance increases. For the two-phase system, two different feature selection techniques were proposed which solves both the linear dependency and non-linear dependency between the features. In the first phase, the RNN combines with the proposed Joint Mutual Information Maximization (JMIM) based feature selection and in the second phase, the Support Vector Machine (SVM) combines with correlation based feature selection. Extensive simulations are carried out over the proposed system using two different datasets, NSL-KDD and Kyoto2006+. The performance is measured through the performance metrics such as Detection Rate (DR), Precision, False Alarm Rate (FAR), Accuracy and F-Score. Furthermore, a comparative analysis with few recent hybrid frameworks is also enumerated. The obtained results signify the effectiveness of proposed method.