在网络空间要素预测过程中加入地理空间特征，可实现时空预测网络空间要素。针对网络安全要素预测过程中少有结合网络数据地理空间特征的研究现状，选择有地理空间特征的网络漏洞检测数据，构造网络漏洞时空数据集，通过构建结合图卷积和门控时间卷积的时空图卷积模型，实现网络漏洞态势发展的预测。选取 ARIMA 和 LSTM 时序预测模型进行对比实验，提出的网络漏洞时空图卷积预测模型在MAE、RMSE和MAPE的评价标准下显示有着更好的预测效果。
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.
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