In view of the increasingly serious problem of network security, geographical space features are added into the prediction process to realize spatio-temporal prediction of network space elements in this study. Considering the research status that network data are often rarely combined with geospatial characteristics in the prediction process of network security elements, network vulnerability detection data with geospatial characteristics are also selected to construct the spatio-temporal data set of network vulnerabilities. By constructing a spatio-temporal graph convolution model combining graph convolution and gated time convolution, the development of network vulnerability situation can be predicted. ARIMA and LSTM temporal prediction models are selected for comparative experiments, and the proposed network vulnerability spatio-temporal graph convolution prediction model shows better prediction effect under MAE, RMSE and MAPE evaluation criteria.

cyberspace data  /  geography space  /  spatio-temporal data  /  spatio-temporal graph convolution  /  prediction model