Safety and security-related event detection in industrial control system using convolutional transformer

Zhaoming Miao; Qinjiang Sun; Cong Jiang; Xiangxiang Chen; Wenhai Wang

doi:10.1117/12.3025589

8 April 2024 Safety and security-related event detection in industrial control system using convolutional transformer

Zhaoming Miao, Qinjiang Sun, Cong Jiang, Xiangxiang Chen, Wenhai Wang

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130900V (2024) https://doi.org/10.1117/12.3025589
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Industrial control systems play a critical role in the operation of industries. It is crucial to ensure the reliability and security of these systems through safety and security-related event detection. As an emerging neural network architecture, Transformer can effectively extract global dependency characteristics. However, existing Transformer-based methods ignore the extraction of local correlation features, which are essential for safety and security-related event detection. To address this issue, in this paper, we introduce a convolutional Transformer model. The proposed model comprises a convolution module and a Transformer module, dedicated to capturing local correlation characteristics and global dependency characteristics, respectively. Comparative experiments are carried out on an industrial control system dataset. Experimental results indicate that the proposed approach achieves superior performance in both three-class and multiclass detection tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhaoming Miao, Qinjiang Sun, Cong Jiang, Xiangxiang Chen, and Wenhai Wang "Safety and security-related event detection in industrial control system using convolutional transformer", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130900V (8 April 2024); https://doi.org/10.1117/12.3025589

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KEYWORDS

Transformers

Safety

Control systems

Convolution

Machine learning

Feature extraction

Head

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