To address the shortage of spatio-temporal feature extraction in network security situation prediction, a local spatio-temporal convolution-based network vulnerability prediction method, namely the local spatio-temporal graph convolutional network model, is proposed, and HA, LSTM, SVR and STGCN models are selected for comparison experiments on network vulnerability data. Experimental results show that the model proposed in this paper can effectively improve the accuracy in predicting the time and location of vulnerabilities as well as the type of network vulnerabilities.

internet security  /  data mining  /  graph convolutional networks  /  spatio-temporal correlation