The security of the rolling stock network is directly related to the stability and security of the railroad transportation system. With the continuous development of the rolling stock network, the network security problem is becoming more and more prominent. Based on this, this paper proposes a security situational awareness model based on multi-source data fusion. First, based on the network topology, host information and alarm information, the fusion framework of multi-source heterogeneous data is established. Second, the network security posture is evaluated and predicted by applying algorithms such as Bayesian network and Kalman filter. The experimental results show that the model can effectively improve the accuracy of security posture assessment when dealing with multi-source data. By comparing the detection results of different methods, the model proposed in this paper shows high accuracy and low false detection rate in a variety of network attack scenarios, especially in the types of attacks such as privacy data stealing and network bandwidth consumption, the recognition effect is most significant. The experimental data show that the proposed method has an error of less than 0.03 during the evaluation process and has good real-time performance and stability. Therefore, the security situational awareness method based on this model can provide more accurate security protection support for the rolling stock network.
