iDetector: A Novel Real-Time Intrusion Detection Solution for IoT Networks

Abstract

The rapid proliferation of Internet of Things (IoT) devices has brought about unprecedented convenience to people’s daily lives. However, this growth has also created opportunities for hackers to launch large-scale botnet attacks using these devices. As a result, it is critical to deploy real-time traffic classifiers on edge gateways to detect network intrusions and improve near-source protection capabilities. To this end, we propose iDetector, a novel real-time intrusion detection solution for IoT networks that is simple in structure and easy to reproduce. iDetector samples network conversations in real-time using a sliding sampling window and generates traffic samples that integrate multiple features. This allows the samples to accurately capture the patterns of each type of traffic. We propose the nonlinear feature transformation (NFT) algorithm based on the prior distribution of traffic features to increase the information entropy of the samples and thereby improve the classification performance. To enable deployment on edge gateways, we propose EdgeNet, a lightweight deep neural network model that utilizes depthwise separable convolution and self-attention mechanism to enhance classification performance while reducing the number of model parameters. Experimental evaluations show that our solution outperforms state-of-the-art deep learning-based solutions in terms of classification performance and has faster classification speed on resource-constrained edge gateways.