A framework for selecting classification models in the intruder detection system using topsis

Abstract

As the network has expanded considerably, security mechanisms are a key issue in networks. Intrusive activities, such as unauthorized access and data manipulation, are increasing. Therefore, the role of the Network Intrusion Detection System (NIDS) in monitoring network traffic for activity and determining whether an intrusion has occurred is very important. The performance of an IDS depends on the selection of the classification model and training data, however, many classifiers generate similar results when measuring performance. The technique of order of preference for similarity to the ideal solution (TOPSIS) is used to select one or more alternatives based on the criteria. The main objective is to present some classification models used in a data set to select the best alternative according to the performance criteria using the TOPSIS method. The deductive method and selection research technique were applied to study the NSL-KDD.