A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION CAPABILITY OF INTRUSION PROTECTION SYSTEMS

Abstract

Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive and unrelated features has a significant impact on the rate of intrusion detection performance. To classify network traffic, whether harmful or normal, the use of machine learning algorithms has been applied in many previous researches. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. An improved model for feature selection is proposed in this paper to increase the accuracy of intrusion detection systems, hence improve the performance of an intrusion protection system. We evaluate the performance of our developed model based on a comparison of several known algorithms as well as other algorithms that use of deep learning with a multi-layered perception algorithm. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches.