A Deep Gated Recurrent Unit based model for wireless intrusion detection system

With the advances and growth of various wireless technologies, it is imperative to implement robust Intrusion Detection Systems (IDS). This paper proposes the implementation of Deep Gated Recurrent Unit (DGRU) Based classifier as well as a wrapper-based feature extraction algorithm for Wireless IDS. We assess the performance of the DRGU IDS using the NSL-KDD benchmark dataset. Furthermore, we compare our framework to several popular algorithms including Artificial Neural Networks, Deep Long-Short Term Memory, Random Forest, Naive Bayes and Feed Forward Deep Neural Networks. The experimental outcomes demonstrate that the DGRU IDS displays a significant increase in performance over existing methods. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.