利用神经网络提高入侵检测系统的性能

Satendra Kumar, Anamika Yadav
{"title":"利用神经网络提高入侵检测系统的性能","authors":"Satendra Kumar, Anamika Yadav","doi":"10.1109/ICACCCT.2014.7019145","DOIUrl":null,"url":null,"abstract":"Rapid growth in Internet and in parallel attacks, vulnerability and threats, has made intrusion detection systems very essential component in all parts of security infrastructure. Building IDS is not a new task, classical signature based IDS are used but they are unable to handle novel attacks. In this paper artificial neural network based intrusion detection is proposed for complete KDD cup 99 dataset. Performance of the proposed ANN based IDS system is evaluated and results shows high anomaly detection accuracy for the complete KDD cup 99 dataset as compared to existing techniques.","PeriodicalId":239918,"journal":{"name":"2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"41","resultStr":"{\"title\":\"Increasing performance Of intrusion detection system using neural network\",\"authors\":\"Satendra Kumar, Anamika Yadav\",\"doi\":\"10.1109/ICACCCT.2014.7019145\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rapid growth in Internet and in parallel attacks, vulnerability and threats, has made intrusion detection systems very essential component in all parts of security infrastructure. Building IDS is not a new task, classical signature based IDS are used but they are unable to handle novel attacks. In this paper artificial neural network based intrusion detection is proposed for complete KDD cup 99 dataset. Performance of the proposed ANN based IDS system is evaluated and results shows high anomaly detection accuracy for the complete KDD cup 99 dataset as compared to existing techniques.\",\"PeriodicalId\":239918,\"journal\":{\"name\":\"2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"41\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACCCT.2014.7019145\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACCCT.2014.7019145","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 41

摘要

随着互联网的快速发展以及并行攻击、漏洞和威胁的日益增多,入侵检测系统已成为安全基础设施中必不可少的组成部分。建立入侵检测系统并不是一项新任务,传统的基于签名的入侵检测系统被使用,但它们无法处理新的攻击。针对KDD cup 99完整数据集,提出了基于人工神经网络的入侵检测方法。对基于人工神经网络的IDS系统的性能进行了评估,结果表明,与现有技术相比,对于完整的KDD cup 99数据集,IDS系统的异常检测精度很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Increasing performance Of intrusion detection system using neural network
Rapid growth in Internet and in parallel attacks, vulnerability and threats, has made intrusion detection systems very essential component in all parts of security infrastructure. Building IDS is not a new task, classical signature based IDS are used but they are unable to handle novel attacks. In this paper artificial neural network based intrusion detection is proposed for complete KDD cup 99 dataset. Performance of the proposed ANN based IDS system is evaluated and results shows high anomaly detection accuracy for the complete KDD cup 99 dataset as compared to existing techniques.
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