A Deep One-Class Intrusion Detection Scheme in Software-Defined Industrial Networks

Bing Hu, Yuanguo Bi, Mingjian Zhi, Kuan Zhang, Feihong Yan, Qian Zhang, Zheng Liu

Abstract: The unprecedented development of intelligent manufacturing requires to customize and change the network traffic strategies frequently. With the advantages of highagility and programmability, software-defined networking can dynamically manage industrial networks, which makes it a promising networking technology for intelligent manufacturing. However, the software-defined industrial network architecture is vulnerable to network attacks, which may degrade manufacturing productivity, and even cause accidents. In this article, we propose a deep learning-based one-class intrusion detection scheme (DO-IDS) to improve the security of industrial networks. Firstly, DO-IDS periodically extracts the flow statistics of the industrial network traffic to generate network status features. Then, it utilizes a deep learning-based dimension reduction approach to filter redundant features. In addition, a deep learning-based one-class detector is designed to calculate the abnormal scores of the network status features. Finally, we conduct extensive simulations, which demonstrates that DO-IDS can detect abnormal traffic with enhanced accuracy and high efficiency.