A semi-supervised deep learning method in network intrusion detection

Abstract

Network intrusion detection system (NIDS) plays an important role in network security. With the continuous development of technology, machine learning and deep learning are gradually becoming the main methods of NIDS. However, a large amount of network traffic data has the problem of manual labeling, which results in a limited train datasets, and reduces the performance of NIDS. Semi-supervised learning is a new approach that combines supervised and unsupervised learning to analyze large unlabeled datasets with a small number of labels. In this paper, we propose a semi-supervised deep learning method, which uses improved tri-training algorithm, and combines with deep learning model. We verified the performance of the proposed method on CICIDS2017 datasets. The experimental results show that the proposed method can improve performance of NIDS and outperform other semi-supervised learning methods.