Back propagation neural network approach to Intrusion Detection System

I. Mukhopadhyay, M. Chakraborty, S. Chakrabarti, Tanusree Chatterjee
{"title":"Back propagation neural network approach to Intrusion Detection System","authors":"I. Mukhopadhyay, M. Chakraborty, S. Chakrabarti, Tanusree Chatterjee","doi":"10.1109/ReTIS.2011.6146886","DOIUrl":null,"url":null,"abstract":"As the Internet is growing - so is the vulnerability of the network. Companies now days are spending huge amount of money to protect their sensitive data from different attacks that they face. In this paper, we propose a new methodology towards developing an Intrusion Detection System (IDS) based on Back-Propagation Neural Network (BPN) model. The proposed system was simulated using Matlab2010a utilizing benchmark intrusion KDDCUP'99 dataset to verify its feasibility and effectiveness.","PeriodicalId":137916,"journal":{"name":"2011 International Conference on Recent Trends in Information Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Recent Trends in Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ReTIS.2011.6146886","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30

Abstract

As the Internet is growing - so is the vulnerability of the network. Companies now days are spending huge amount of money to protect their sensitive data from different attacks that they face. In this paper, we propose a new methodology towards developing an Intrusion Detection System (IDS) based on Back-Propagation Neural Network (BPN) model. The proposed system was simulated using Matlab2010a utilizing benchmark intrusion KDDCUP'99 dataset to verify its feasibility and effectiveness.
入侵检测系统的反向传播神经网络方法
随着互联网的发展,网络的脆弱性也随之增加。如今,公司花费大量资金来保护他们的敏感数据免受各种攻击。本文提出了一种基于反向传播神经网络(BPN)模型的入侵检测系统开发方法。利用基准入侵KDDCUP’99数据集,利用Matlab2010a对系统进行了仿真,验证了系统的可行性和有效性。
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:604180095
Book学术官方微信