Traffic Analysis Based Intrusion Dection System For Wireless Systems Using Gated RNN

Abstract

Traffic analysis plays most important role in validating the performance and protection of the whole network environment. As the congestion of network traffic increasing day by day, network traffic analysis need to be practiced periodically for ensuring and enhancing security. Various kinds of analysis methods are proposed. In recent machine learning techniques are employed for detecting intrusions, analysing the behaviours of malwares and discriminating the network traffic. Detecting anomalies in certain period of time is considered as drawback of ML bases analysis techniques and it increased complexity also. In this proposed work, Gated Recurrent Neural Network is incorporated in order to analyse the traffic of the network effectively with aim of zero vulnerability. It overcomes the issues of earlier existing methods and provides better result while comparing with them. In addition, this proposed approach provides better accuracy in term of classifying vulnerabilities in network traffic.