Intrusion Detection and Network Information Security Based on Deep Learning Algorithm in Urban Rail Transit Management System

The exploration of the intrusion detection effect of urban rail transit management system aims to further improve the safety performance of the traffic field in urban construction. Thus, the deep convolution neural network model AlexNet with more network layers and stronger learning ability is adopted and improved, to ensure the safe operation of urban rail transit. Meanwhile, the GRU (Gate Recurrent Unit) neural network is introduced into the improved AlexNet to build an intrusion detection model of urban rail transit management system. Finally, the model performance is verified through the collected data and simulation experiments. Through the comparative analysis of the model and other scholars' models in related fields, the recognition accuracy of intrusion detection of the intrusion detection model reaches 96.00%, which is at least 1.55% higher than that of other neural network models. Besides, its training time is stable at about 55.05 seconds, and the test time is stable at about 22.17 seconds. Moreover, the analysis result of data transmission security performance indicates that the data message delivery rate of this model is more than 80%, the data message leakage rate and packet loss rate are less than 10%, and the average delay is basically stable at about 350 milliseconds. Therefore, the constructed model can achieve high data transmission security performance under the premise of ensuring prediction accuracy, which can provide experimental basis for improving the safety performance of rail transit systems in smart cities.

Automated summary

The study aims to enhance the safety performance of urban construction in the traffic field by utilizing the deep convolution neural network model AlexNet with more network layers and stronger learning ability, and introducing the Gate Recurrent Unit (GRU) neural network into the improved AlexNet to construct an intrusion detection model for the urban rail transit management system. By verifying the model performance with collected data and simulation experiments, it achieves a recognition accuracy of 96.00% for intrusion detection, which is at least 1.55% higher than other neural network models. Furthermore, the model demonstrates stable training and test times as well as high data transmission security performance, providing an experimental basis for improving the safety performance of rail transit systems in smart cities.