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.
ZHANG Xun
,
ZHANG Chutong
,
EZIZ Tursun
,
HAO Mengmeng
,
ZHANG Yingchun
,
JIANG Dong
. A local spatio-temporal graph convolution-based approach and its application to network vulnerability prediction[J]. Science & Technology Review, 2023
, 41(13)
: 67
-75
.
DOI: 10.3981/j.issn.1000-7857.2023.13.007
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