DeepNIDS: A Deep Neural Network-Based Network Intrusion Detection System for IoT

Recently, the widespread use of Internet of Things (IoT) has been triggering an exponential increase in the number of smart devices lacking hardware security supports. This gives rise to various challenges in Cyber-threat protection. In this paper, we present DeepNIDS: a lightweight neural networkbased network intrusion detection system (NIDS) that effectively detects abnormal traffic. The core algorithm of DeepNIDS is a novel Convolutional Neural Network (CNN) model, specifically designed for classifying network traffic patterns. To enhance the detection performance, we employ 2D reshaped features as the input of our model, which is extracted and reshaped from network traffic over a period using Damped Incremental Statistics algorithm. Our experimental results show that DeepNIDS could identify nine types of attacks, showing superior detection capabilities over existing NIDS, with an average accuracy of approximately 98.48%.