Increasing the rate of intrusion detection based on a hybrid technique

Khattab M. Ali Alheeti, L. Al-Jobouri, K. Mcdonald-Maier
{"title":"Increasing the rate of intrusion detection based on a hybrid technique","authors":"Khattab M. Ali Alheeti, L. Al-Jobouri, K. Mcdonald-Maier","doi":"10.1109/CEEC.2013.6659468","DOIUrl":null,"url":null,"abstract":"This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEEC.2013.6659468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.
提高基于混合技术的入侵检测率
本文提出了提高入侵检测率的技术。这些技术是基于被检测到的特定特征,并且已经证明,与大量特征相比,少量特征(9)可以产生更高的检测率。这些技术利用软计算技术,如基于反向传播的人工神经网络和模糊集。这些技术实现了对标准DARPA基准数据的技术状态的显著改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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