To accurately detect and identify whether there are abnormalities in the information of the expressway networking system, an information security event reasoning and traceability method based on deep learning is proposed to build a data security protection system that includes the data life periodicity of the expressway networking system. In this system, the information security event model based on intrusion detection message exchange format is established. The model uses the information risk event reasoning method based on a deep convolution neural network to infer the risk event during data sharing of the expressway networking system, reorganize and standardize the risk event information according to the format of the information security event standardization model, and store it in the risk event database in the form of Extensible Markup Language data document. The information risk event traceability method based on the electronic fingerprint takes all risk events in the database as the target. After designing the electronic fingerprint of risk events, the original network attack tree is constructed to realize risk event traceability combined with fingerprint information. Testing indicates that the reasoning and traceability results of this method to the information security events of the expressway networking system are consistent with those in reality and our method has good usability.

To ensure the reliability and safety of expressway networking systems, this paper designs a data flow risk monitoring system for expressway networking systems based on deep learning. The monitoring system is composed of data flow risk analysis, formulation of safety strategy, real-time monitoring, and disaster recovery. Data flow risk analysis is the basis for the operation of each part of the system. Meanwhile, indexes such as network management, data assets, and network resources are selected to build a data flow risk monitoring index system. The deep convolution neural network model is constructed, and the data flow risk monitoring index data are input into the deep convolution neural network to extract the index data features through the convolution and pooling process. Based on this, feature mapping is realized with a multilayer perceptron, and the index data risk classification results of data flow risk monitoring are output by the SoftMax classifier. The experimental results show that the monitoring system can obtain accurate data flow risk analysis results which effectively reduces the data loss rate, alleviate the impact of different types of malicious attacks, and ensure the stability and security of the experimental object.