Research on Rail Transit Network Security Threat Identification Method Based on Deep Learning
With the development of information technology, communication based train control signal systems (CBTC) and integrated supervisory control systems (ISCS) in the field of rail transit are deeply integrated with infor-mation technology. More and more systems and control networks adopt Ethernet protocol, which leads to increasingly high network security risks. Aiming at the communication characteristics of rail transit networks, a multi- dimensional i-dentification method is proposed for rail transit network security threats based on deep learning. The original packets, network logs, and configuration information of rail transit network dedicated protocol network traffic are comprehen-sively extracted, and a multi- dimensional deep learning identification model for rail transit network attack traffic is constructed by using the extracted information as the input. The obtained rail transit network traffic characteristics are more reasonable and accurate, and can effectively improve the accuracy of rail transit network attack/abnormal traffic identification without affecting the normal business operation of the target network.
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