A Lightweight Semi-Supervised Learning Method Based on Consistency Regularization for Intrusion Detection

Ruijie Zhao, Tiantian Tang, Guan Gui, Zhi Xue

Abstract: With the development of the Industrial Internet of Things (IIoT), more frequent attacks occur to intrude IIoT devices. A reasonably designed intrusion detection method can effectively guarantee the security of IIoT. Over the past decade, different methods of intrusion detection based on deep learning (DL) have been proposed, which helps intrusion detection keep evolving and become more robust. However, these previous researches usually require the participation of a large number of experts, and gradually become invalid with the continuous development of intrusion methods. The limited compute capability of IIoT devices also greatly hinder the deployment of overly complex DL models. To address these challenges, this paper proposes a lightweight semi-supervised learning (LSSL) method based on consistency regularization for intrusion detection. Our proposed method enhances the detection performance by using unlabeled traffic data for consistency training. Besides, we adopt separable convolutions for efficient feature extraction. Experimental results on two widely-used benchmark datasets show that the detection performance of our model is significantly improved by the consistency training, and it can effectively detect various attacks in complex networks.