A NETWORK INTRUSION DETECTION SYSTEM FOR IoT USING MACHINE LEARNING AND DEEP LEARNING APPROACHES

Abstract

Internet of Things (IoT) makes physical objects and devices interact with each other through wireless technologies. IoT is expected to deliver a significant role in our lives in near future. However, at the current stage, IoT is vulnerable to various kinds of security threats just like other wired and wireless networks. The main focus of this work is protecting an IoT infrastructure from various kinds of attacks generated by the intruders. In this approach, Naïve Bayes classification algorithm is applied for intrusion detection systems (IDSs). And the results obtained are compared with the results generated by deploying a deep learning model.