Intrusion Detection of Industrial Control System Based on Stacked Auto-Encoder

Title	Intrusion Detection of Industrial Control System Based on Stacked Auto-Encoder
Publication Type	Conference Paper
Year of Publication	2019
Authors	Zhang, Rui, Chen, Hongwei
Conference Name	2019 Chinese Automation Congress (CAC)
Date Published	Nov. 2019
Publisher	IEEE
ISBN Number	978-1-7281-4094-0
Keywords	Analytical 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
Abstract	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%.
URL	https://ieeexplore.ieee.org/document/8997243
DOI	10.1109/CAC48633.2019.8997243
Citation Key	zhang_intrusion_2019