Abstract
The power grid as the national manufacture steps toward combination with advanced information technology. The industrial control system of the power grid is exposed to the wide-opening internet with the industrial internet rapidly developing. Traffic classification is the significant step for security situation awareness platform which supervises the ICS operating status and suffers from the defect of low classification accuracy by conventional port-based or DPI methods. Therefore, the two-dimensional CNN model for the power grid industrial control traffic classification is proposed in this paper, which extracts the raw data’s features to train a two-dimensional CNN to fit the distribution of the features. The experiment’s result shows that this model can recognize and classify the ICS traffic accurately with an accuracy of 94%. Through the cross-validation, the result shows that this model also has outstanding generalization ability with an accuracy of 93%.
G. Yue—Supported by 2020 Industry internet innovation and development project - Smart energy internet security situation awareness platform project.
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Yue, G., sun, Z., Tian, J., Zhu, H., Zhang, B. (2022). Power Grid Industrial Control System Traffic Classification Based on Two-Dimensional Convolutional Neural Network. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_6
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DOI: https://doi.org/10.1007/978-981-19-0390-8_6
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