Sliding window assisted mutual redundancy-based feature selection for intrusion detection system. (14th June 2022) - Veeranna, Thotakura; Reddy, Kiran Kumar
In this paper, we propose a sliding windowing based mutual redundancy assisted feature selection (MRFS) algorithm that finds the duplicate features analytically and selects optimal feature for detection. This MRFS evaluates the mutual redundancy between as well as within network traffic connections and then selects an optimal feature subset to represent each connection attribute. After feature selection, they are fed to multi-class support vector machine (MC-SVM) based IDS for classification. The performance of MRFS-IDS is evaluated using a standard intrusion dataset, i.e., NSL-KDD and performance is measured in terms of accuracy and false alarm rate. The simulation results demonstrate that the proposed MRFS-IDS model has gained an accuracy of 95% approximately and false alarm rate of 0.70% which is much better than the counterpart methods.