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With the development of industrial control network and the deep integration of industry and information technology, the rapid development of industrial control system has increased dramatically, which has brought huge economic and property losses to industrial control companies. Therefore, a traffic identification technology based on deep learning is proposed, which makes full use of the characteristics of industrial network traffic signs. Combined with experiments, this technology can classify network traffic and effectively identify abnormal traffic in industrial control system network. Compared with traditional classification methods, it not only improves the accuracy of traffic identification, but also reduces the time required for classification.

Research on Traffic Recognition Algorithms for Industrial Control Networks based on Deep Learning