Identification of industrial control devices based on CNN-PCA hybrid feature extraction

Ziqiang Zhou; Shan Liu; Yao Wang; Yu Zhang; Dazhe Yang

doi:10.1117/12.2640735

13 October 2022 Identification of industrial control devices based on CNN-PCA hybrid feature extraction

Ziqiang Zhou, Shan Liu, Yao Wang, Yu Zhang, Dazhe Yang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122872F (2022) https://doi.org/10.1117/12.2640735
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

With the development of information technology and intelligent manufacturing technology, more and more industrial control system (ICS) devices with IP address can be accessed through the Internet. In order to quickly search and monitor the networked industrial control device or system, and master the type, scale and regional distribution of the device, it is urgent to use relevant technologies to identify the networked industrial control system or device. In this paper, we first analyze the industrial control protocols widely used in industrial control systems and the special fields in the protocols. Then, we design a detection scheme to collect the data packets of ICS device, and select the specific word segment of industrial control protocol according to the diversity and consistency of field values. Finally, we propose an automatic detection system for industrial control device, which uses convolutional neural network (CNN) and principal component analysis (PCA) to extract feature field and construct feature fingerprint, and uses limit learning machine (ELM) to realize fine-grained ICS device recognition.

Citation Download Citation

Ziqiang Zhou, Shan Liu, Yao Wang, Yu Zhang, and Dazhe Yang "Identification of industrial control devices based on CNN-PCA hybrid feature extraction", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122872F (13 October 2022); https://doi.org/10.1117/12.2640735

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