Article(id=1242140532470784574, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242140525369831525, articleNumber=null, orderNo=9, doi=10.3981/j.issn.1000-7857.2023.13.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1676908800000, receivedDateStr=2023-02-21, revisedDate=1682352000000, revisedDateStr=2023-04-25, 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=1774078980793, creator=sys-migrate, updateTime=1774078980793, 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=67, endPage=75, ext={EN=ArticleExt(id=1242140534920261799, articleId=1242140532470784574, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=A local spatio-temporal graph convolution-based approach and its application to network vulnerability prediction, columnId=1242140529929040008, journalTitle=Science & Technology Review, columnName=Exclusive: Theory and Application of Cyberspace Geography, runingTitle=null, highlight=null, articleAbstract=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., authors=ZHANG Xun1,2 , ZHANG Chutong1 , EZIZ Tursun2 , HAO Mengmeng3 , ZHANG Yingchun4* , JIANG Dong3 , authorsList=ZHANG Xun, ZHANG Chutong, EZIZ Tursun, HAO Mengmeng, ZHANG Yingchun, JIANG Dong, authorCompany=1. School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China1,2 ，张楚童1 ，艾孜孜·吐尔逊2 ，郝蒙蒙3 ，张迎春4* ，江东3 , authorsList=张珣，张楚童，艾孜孜·吐尔逊，郝蒙蒙，张迎春，江东, authorCompany=1. 北京工商大学计算机学院，北京 100048
科技导报
| 专题：网络空间地理学理论与应用 2023, 41(13): 67-75
一种局部时空图卷积方法及其在网络漏洞预测的应用
全屏
张珣1,2 ，张楚童1 ，艾孜孜·吐尔逊2 ，郝蒙蒙3 ，张迎春4* ，江东3
作者信息
1. 北京工商大学计算机学院，北京 100048
通讯作者:
张迎春（通信作者），实验师，研究方向为控制科学、地理信息，电子信箱：zhangyingchun@btbu.edu.cn
A local spatio-temporal graph convolution-based approach and its application to network vulnerability prediction
Affiliations
出版时间: 2023-07-13
doi: 10.3981/j.issn.1000-7857.2023.13.007
文章导航
针对网络安全态势预测中时空特征提取的不足，提出了一种基于局部时空卷积的网络漏洞预测方法，即局部时空图卷积网络模型，并针对网络漏洞数据选取历史平均法、长短期记忆网络、支持向量回归、时空图卷积网络模型进行对比实验。实验结果表明，提出的局部时空图卷积网络模型能够有效提高预测漏洞的时间、位置以及网络漏洞类型的准确度。
网络安全
/
数据挖掘
/
图卷积神经网络
/
时空相关性
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.
internet security
/
data mining
/
graph convolutional networks
/
spatio-temporal correlation
张珣，张楚童，艾孜孜·吐尔逊，郝蒙蒙，张迎春，江东.
一种局部时空图卷积方法及其在网络漏洞预测的应用.
科技导报,
2023
, 41
(13)
: 67
-75
.
DOI: 10.3981/j.issn.1000-7857.2023.13.007
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
2023年第41卷第13期
PDF下载
332
82
引用本文
BibTeX
文章信息
doi: 10.3981/j.issn.1000-7857.2023.13.007
接收时间：2023-02-21
首发时间：2023-08-11
出版时间：2023-07-13
收稿日期：2023-02-21
修回日期：2023-04-25
通讯作者:
张迎春（通信作者），实验师，研究方向为控制科学、地理信息，电子信箱：zhangyingchun@btbu.edu.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2023.13.007
引用本文
BibTeX
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
关闭全屏
BibTeX
EndNote
RefWorks
TxT
PDF下载
全屏
332
82
关闭全屏
北京市海淀区学院南路86号
010-62199257
qkjq@cast.org.cn
