Visible to the public Intrusion Detection of Industrial Control System Based on Stacked Auto-Encoder

TitleIntrusion Detection of Industrial Control System Based on Stacked Auto-Encoder
Publication TypeConference Paper
Year of Publication2019
AuthorsZhang, Rui, Chen, Hongwei
Conference Name2019 Chinese Automation Congress (CAC)
Date PublishedNov. 2019
ISBN Number978-1-7281-4094-0
KeywordsAnalytical models, anomaly detection, Data models, feature extraction, ICs, industrial control, industrial control system, industrial control systems, industrial production, Internet technologies, Intrusion detection, intrusion detection model, Multi-classification support vector machine, multiclassification support vector machine, network data feature extraction, neural nets, pattern classification, production engineering computing, pubcrawl, resilience, Resiliency, Scalability, security of data, stacked auto-encoder, Support vector machines, Training

With the deep integration of industrial control systems and Internet technologies, how to effectively detect whether industrial control systems are threatened by intrusion is a difficult problem in industrial security research. Aiming at the difficulty of high dimensionality and non-linearity of industrial control system network data, the stacked auto-encoder is used to extract the network data features, and the multi-classification support vector machine is used for classification. The research results show that the accuracy of the intrusion detection model reaches 95.8%.

Citation Keyzhang_intrusion_2019