基于监督学习的智能入侵检测系统

Sandipan Roy, Apurbo Mandal, Debraj Dey
{"title":"基于监督学习的智能入侵检测系统","authors":"Sandipan Roy, Apurbo Mandal, Debraj Dey","doi":"10.21467/proceedings.115.3","DOIUrl":null,"url":null,"abstract":"Going digital involves networking with so many connected devices, so network security becomes a critical task for everyone. But an intrusion detection system can help us to detect malicious activity in a system or network. But generally, intrusion detection systems (IDS) are not reliable and sustainable also they require more resources. In recent years so many machine learning methods are proposed to give higher accuracy with minimal false alerts. But analyzing those huge traffic data is still challenging. So, in this article, we proposed a technique using the Support Vector Machine & Naive Bayes algorithm, by using this we can solve the classification problem of the intrusion detection system. For evaluating our proposed method, we use NSL-KDD and UNSW-NB15 dataset. And after getting the result we see that the SVM works better than the Naive Bayes algorithm on that dataset.","PeriodicalId":413368,"journal":{"name":"Proceedings of Intelligent Computing and Technologies Conference","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent Intrusion Detection System using Supervised Learning\",\"authors\":\"Sandipan Roy, Apurbo Mandal, Debraj Dey\",\"doi\":\"10.21467/proceedings.115.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Going digital involves networking with so many connected devices, so network security becomes a critical task for everyone. But an intrusion detection system can help us to detect malicious activity in a system or network. But generally, intrusion detection systems (IDS) are not reliable and sustainable also they require more resources. In recent years so many machine learning methods are proposed to give higher accuracy with minimal false alerts. But analyzing those huge traffic data is still challenging. So, in this article, we proposed a technique using the Support Vector Machine & Naive Bayes algorithm, by using this we can solve the classification problem of the intrusion detection system. For evaluating our proposed method, we use NSL-KDD and UNSW-NB15 dataset. And after getting the result we see that the SVM works better than the Naive Bayes algorithm on that dataset.\",\"PeriodicalId\":413368,\"journal\":{\"name\":\"Proceedings of Intelligent Computing and Technologies Conference\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Intelligent Computing and Technologies Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21467/proceedings.115.3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Intelligent Computing and Technologies Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21467/proceedings.115.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

数字化涉及到与如此多的连接设备联网,因此网络安全成为每个人的关键任务。但是入侵检测系统可以帮助我们检测系统或网络中的恶意活动。但入侵检测系统的可靠性和可持续性较差,对资源的要求较高。近年来,人们提出了许多机器学习方法,以提供更高的准确性和最小的错误警报。但分析这些庞大的交通数据仍然具有挑战性。因此,本文提出了一种基于支持向量机和朴素贝叶斯算法的分类方法,利用该方法可以解决入侵检测系统的分类问题。为了评估我们提出的方法,我们使用NSL-KDD和UNSW-NB15数据集。在得到结果之后,我们看到支持向量机在数据集上比朴素贝叶斯算法工作得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Intrusion Detection System using Supervised Learning
Going digital involves networking with so many connected devices, so network security becomes a critical task for everyone. But an intrusion detection system can help us to detect malicious activity in a system or network. But generally, intrusion detection systems (IDS) are not reliable and sustainable also they require more resources. In recent years so many machine learning methods are proposed to give higher accuracy with minimal false alerts. But analyzing those huge traffic data is still challenging. So, in this article, we proposed a technique using the Support Vector Machine & Naive Bayes algorithm, by using this we can solve the classification problem of the intrusion detection system. For evaluating our proposed method, we use NSL-KDD and UNSW-NB15 dataset. And after getting the result we see that the SVM works better than the Naive Bayes algorithm on that dataset.
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:604180095
Book学术官方微信