Article(id=1242140532894409280, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242140525369831525, articleNumber=null, orderNo=8, doi=10.3981/j.issn.1000-7857.2023.13.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1670774400000, receivedDateStr=2022-12-12, revisedDate=1682179200000, revisedDateStr=2023-04-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1691720709757, onlineDateStr=2023-08-11, pubDate=1689177600000, pubDateStr=2023-07-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1691720709757, onlineIssueDateStr=2023-08-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078980893, creator=sys-migrate, updateTime=1774078980893, updator=sys-migrate, issue=Issue{id=1242140525369831525, tenantId=1146029695717560320, journalId=1146031591421210625, year='2023', volume='41', issue='13', pageStart='1', pageEnd='128', issueExtLink='null', onlineDate='null', pubDate='1689177600000', pubDateStr='2023-07-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078979100, creator='sys-migrate', updateTime=1774078979100, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=60, endPage=66, ext={EN=ArticleExt(id=1242140535419383984, articleId=1242140532894409280, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Network vulnerability situation prediction based on spatio-temporal graph convolution, columnId=1242140529929040008, journalTitle=Science & Technology Review, columnName=Exclusive: Theory and Application of Cyberspace Geography, runingTitle=null, highlight=null, articleAbstract=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., authors=ZHANG Yingchun¹, LI Jin², ABDUREYIM Raxidin³, ZHANG Xun^2,3*, HAO Mengmeng⁴, JIANG Dong⁴, authorsList=ZHANG Yingchun, LI Jin, ABDUREYIM Raxidin, ZHANG Xun, HAO Mengmeng, JIANG Dong, authorCompany=1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
2. School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China
3. School of Mathematics and Information, Hotan Normal College, Hotan 848099，China
4. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=FLaNITzDb22iccTRPqgfSw==, pdfFileSize=1383824, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242140534391775824, articleId=1242140532894409280, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于时空图卷积的网络漏洞态势预测, columnId=1242140528909824124, journalTitle=科技导报, columnName=专题：网络空间地理学理论与应用, runingTitle=null, highlight=null, articleAbstract=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。, authors=张迎春¹，李金²，阿布都热依木·热西丁³，张珣^2,3*，郝蒙蒙⁴，江东⁴, authorsList=张迎春，李金，阿布都热依木·热西丁，张珣，郝蒙蒙，江东, authorCompany=1. 北京工商大学人工智能学院，北京 100048
2. 北京工商大学计算机学院，北京 100048
3. 和田师范专科学校数学与信息学院，和田 848099
4. 中国科学院地理科学与资源研究所，北京 100101, correspAuthors=null, authorNote=张迎春，实验师，研究方向为网络安全、地理人工智能，电子信箱：zhangyingchun@btbu.edu.cn, correspAuthorsNote=张珣（通信作者），教授，研究方向为网络安全、地理人工智能，电子信箱：zhangxun@btbu.edu.cn, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=QZjLxyJHqDNGVywffMiPvQ==, pdfFileSize=1383824, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=国家重点研发计划项目（2020YFB1806500）)}, authors=null, keywords=[Keyword(id=1242140533892653641, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242140532894409280, language=CN, orderNo=1, keyword=网络空间数据), Keyword(id=1242140533972345419, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242140532894409280, language=CN, orderNo=1, keyword=地理空间), 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