Improved Feature Selection Algorithm for Intrusion Detection Using Data Mining Approach

Abstract

With the rapid growth of Internet applications, there are more and more intrusions into network systems. In this case, it is necessary to provide security for the network through effective intrusion detection and prevention methods. This can mainly be achieved by creating effective interruption detection systems using efficient algorithms that can identify abnormal activities in network traffic and safeguard network resources from unlawful attack by interlopers. Although many interruption recognition frameworks have been proposed before, existing network intrusion detection has limitations in terms of accuracy and detection time. To overcome these shortcomings, In this paper, we propose a new intrusion detection system by developing a new intelligent feature selection algorithm based on conditional random fields (CRF) to optimize the number of features. Furthermore, algorithms based on existing hierarchical methods (LA) In this paper, we propose another interrupt recognition framework, fostering a book. Compared with the existing methods, the interruption identification framework provides high precision and achieves the efficiency of attack detection. The main advantages of this system are reduced detection time, improved classification accuracy and lower false positive rate.