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 Xun2,3* , 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, China1 ，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
科技导报
| 专题：网络空间地理学理论与应用 2023, 41(13): 60-66
基于时空图卷积的网络漏洞态势预测
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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
作者信息
1. 北京工商大学人工智能学院，北京 100048
通讯作者:
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
Network vulnerability situation prediction based on spatio-temporal graph convolution
Affiliations
出版时间: 2023-07-13
doi: 10.3981/j.issn.1000-7857.2023.13.006
文章导航
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。
网络空间数据
/
地理空间
/
时空数据
/
时空图卷积
/
预测模型
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
张迎春，李金，阿布都热依木·热西丁，张珣，郝蒙蒙，江东.
基于时空图卷积的网络漏洞态势预测.
科技导报,
2023
, 41
(13)
: 60
-66
.
DOI: 10.3981/j.issn.1000-7857.2023.13.006
ZHANG Yingchun, LI Jin, ABDUREYIM Raxidin, ZHANG Xun, HAO Mengmeng, JIANG Dong.
Network vulnerability situation prediction based on spatio-temporal graph convolution[J].
Science & Technology Review ,
2023
, 41
(13)
: 60
-66
.
DOI: 10.3981/j.issn.1000-7857.2023.13.006
2023年第41卷第13期
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文章信息
doi: 10.3981/j.issn.1000-7857.2023.13.006
接收时间：2022-12-12
首发时间：2023-08-11
出版时间：2023-07-13
收稿日期：2022-12-12
修回日期：2023-04-23
通讯作者:
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
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2023.13.006
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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