基于变分自编码器的MVB网络异常检测方法

多功能车辆总线(MVB)用于列车通信网络中各功能设备间的信息传输,其网络异常将严重影响列车运行安全.在对MVB网络常见故障分析的基础上,提出一种基于变分自编码器(VAE)的MVB网络异常检测方法,直接将采集到的MVB信号物理波形作为模型输入,选取VAE重构误差作为MVB网络异常检测依据.为了有效解决实际应用中带标记异常...

Anomaly Detection Method for MVB Network Based on Variational Autoencoder

Multifunction Vehicle Bus(MVB)is used to transfer information among devices in the train communication network,whose anomaly will endanger the safety of train operation.Based on the analysis of MVB common faults,an anomaly detection method was proposed for the MVB network based on variational autoencoder(VAE),where the physical layer waveforms of MVB signals collected were directly used as input of VAE model and the reconstruction error of the VAE model was defined as the basis of the MVB network anomaly detection.In the training phase,the VAE model was trained by only using the normal data in the semi-supervised learning manner,which can solve the problem of the lack of labeled anomaly samples in practice.The health indicator of the MVB network node was designed according to the reconstruction error of the normal data of the MVB network,and the kernel density estimation method was applied to determine the decision threshold in this case only normal samples were provided without relying on expert experience.The experimental results show that the proposed method,capable of handling the high-dimensional samples and learning the internal features of the MVB waveforms effectively,has higher performance than the traditional methods in anomaly detection.