Article(id=1242118924163154804, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242118891057513199, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1251043200000, receivedDateStr=2009-08-24, revisedDate=-2209017600000, revisedDateStr=1900-01-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1126281600000, onlineDateStr=2005-09-10, pubDate=1126281600000, pubDateStr=2005-09-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1126281600000, onlineIssueDateStr=2005-09-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073828971, creator=sys-migrate, updateTime=1774073828971, updator=sys-migrate, issue=Issue{id=1242118891057513199, tenantId=1146029695717560320, journalId=1146031591421210625, year='2005', volume='23', issue='0509', pageStart='1', pageEnd='79', issueExtLink='null', onlineDate='null', pubDate='1126281600000', pubDateStr='2005-09-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774073821079, creator='sys-migrate', updateTime=1774073821079, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=11, endPage=13, ext={EN=ArticleExt(id=1242118927363412326, articleId=1242118924163154804, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Detecting Distributed Denial of Service Attacks Based on Load Prediction, columnId=1150494642224591153, journalTitle=Science & Technology Review, columnName=Exclusive, runingTitle=null, highlight=null, articleAbstract=By analyzing of the features of distributed denial of service(DDOS) attacks, a novel approach of detection of DDOS attacks based on host load-concurrent connection time series prediction is proposed. This method has improved the traditional anomaly detection. It predicts concurrent connection time series and adopts prediction results as normal host load estimate of next time range. So the timeliness of normal behavior description is improved; high detection accuracy and low detection latency are acquired. Two key technologies are studied: one is load prediction technology; the other is load anomaly judgment. In order to improve prediction accuracy, a novel wavelet-BP neural network model is established and in order to increase anomaly judgment accuracy, "sliding window" method is used. Experiment results show that our new method is better than traditional anomaly detection technologies., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=aZnPR/aXwwhfUJj8MitCdw==, pdfFileSize=301585, 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=1242118926163837831, articleId=1242118924163154804, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于负载预测的分布式拒绝服务攻击检测方法研究, columnId=1150494644438708440, journalTitle=科技导报, columnName=本刊专稿, runingTitle=null, highlight=null, articleAbstract=通过分析分布式拒绝服务攻击(Distributed Denial Of Service,DDOS)的特点,提出一种基于主机负载-并发连接时间序列预测的DDOS攻击检测方法。该方法改进了传统的异常检测方法,对并发连接序列进行预测,以预测值作为对未来时段内主机负载正常状态的估计,增强了正常行为描述的时效性,提高了攻击检测率,并具有低延时特性。该方法涉及两项关键技术:一是预测技术,二是异常判断方法。为提高预测精度,首次将小波分析引入主机负载预测,建立了小波-神经网络预测模型;为提高异常判断准确性,采用了“滑动窗口”方式。实验表明,基于负载预测的DDOS攻击检测优于传统的异常检测方法。, authors=姚淑萍¹,胡昌振², authorsList=姚淑萍;胡昌振, authorCompany=1. 北京理工大学信息安全与对抗技术研究中心 北京2. 北京理工大学网络安全与信息对抗技术研究中心 北京, correspAuthors=null, authorNote=null, correspAuthorsNote=姚淑萍, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=cDI1Ez+6bgLDwc6iUsiN1g==, pdfFileSize=301585, 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)}, authors=null, keywords=[Keyword(id=1242118925798933378, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242118924163154804, language=CN, 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