A deep learning-based system for IoT intrusion detection

Abstract

The Internet of Things devices has rapidly increased and been widely used in recent years. The era of the Internet of Everything is quietly coming, which puts forward higher requirements for the research on network traffic classification in the Internet of Things environment. However, traffic in the network layer and link layer is often ignored. This paper proposes a network traffic classification and feature extraction tool that covers multiple layers of network protocols to convert the original network traffic into digital features. With the features, two deep neural network models constructed were trained, and evaluation of their multiple indicators proved the effectiveness and superiority of our proposed intrusion detection system for IoT. It can achieve a classification accuracy of 98% and 97% of detection rate.