动态演化系统及其在入侵检测系统中的应用

Xian-Ming Xu, J. Zhan
{"title":"动态演化系统及其在入侵检测系统中的应用","authors":"Xian-Ming Xu, J. Zhan","doi":"10.1109/ISA.2008.82","DOIUrl":null,"url":null,"abstract":"In this paper, we present a dynamic evolution system and build up a model to trace the transition of the system state. This new model differs from the previous methods, such as Bayesian network, artificial neural network, in two aspects: it can adapt the changes of the environment automatically, and it does not need a special training phase to build up a model. Theoretical analysis shows that it is applicable and practical, and furthermore, experimental results show that it has good performance especially in dynamic environment.","PeriodicalId":212375,"journal":{"name":"2008 International Conference on Information Security and Assurance (isa 2008)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Dynamic Evolution Systems and Applications in Intrusion Detection Systems\",\"authors\":\"Xian-Ming Xu, J. Zhan\",\"doi\":\"10.1109/ISA.2008.82\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a dynamic evolution system and build up a model to trace the transition of the system state. This new model differs from the previous methods, such as Bayesian network, artificial neural network, in two aspects: it can adapt the changes of the environment automatically, and it does not need a special training phase to build up a model. Theoretical analysis shows that it is applicable and practical, and furthermore, experimental results show that it has good performance especially in dynamic environment.\",\"PeriodicalId\":212375,\"journal\":{\"name\":\"2008 International Conference on Information Security and Assurance (isa 2008)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Information Security and Assurance (isa 2008)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISA.2008.82\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Information Security and Assurance (isa 2008)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISA.2008.82","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文提出了一个动态演化系统,并建立了一个跟踪系统状态转变的模型。这种新模型与以往的贝叶斯网络、人工神经网络等方法的不同之处在于:它可以自动适应环境的变化,不需要专门的训练阶段来建立模型。理论分析表明了该方法的适用性和实用性,实验结果表明该方法在动态环境下具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Evolution Systems and Applications in Intrusion Detection Systems
In this paper, we present a dynamic evolution system and build up a model to trace the transition of the system state. This new model differs from the previous methods, such as Bayesian network, artificial neural network, in two aspects: it can adapt the changes of the environment automatically, and it does not need a special training phase to build up a model. Theoretical analysis shows that it is applicable and practical, and furthermore, experimental results show that it has good performance especially in dynamic environment.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信