基于集成学习分析的入侵检测系统

Anil Kumar Dasari, Saroj K. Biswas, Saptarsi Sanyal, B. Purkayastha
{"title":"基于集成学习分析的入侵检测系统","authors":"Anil Kumar Dasari, Saroj K. Biswas, Saptarsi Sanyal, B. Purkayastha","doi":"10.1109/I2CT57861.2023.10126368","DOIUrl":null,"url":null,"abstract":"An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intrusion Detection System using Ensemble Learning Analytics\",\"authors\":\"Anil Kumar Dasari, Saroj K. Biswas, Saptarsi Sanyal, B. Purkayastha\",\"doi\":\"10.1109/I2CT57861.2023.10126368\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.\",\"PeriodicalId\":150346,\"journal\":{\"name\":\"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2CT57861.2023.10126368\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2CT57861.2023.10126368","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

入侵检测系统(IDS)监视和分析数据,以发现对系统或网络的任何入侵。网络以巨大的数量、种类和速度生成数据,使得使用传统技术(如病毒检测系统、误用检测软件,即用于比较数据包的攻击签名数据库)来检测攻击变得困难。尽管研究人员付出了巨大的努力,IDS仍在努力识别新的入侵,提高检测准确性,降低误报率。为了克服上述问题,本文提出了一种独特的基于机器学习分析的入侵检测系统模型(IDSMLA),该模型使用SMOTE过采样技术来处理类不平衡问题,并使用最小冗余最大相关性(mRMR)来进行特征选择,因为特征选择通过消除不相关特征来降低时间复杂度,从而提高模型的准确性,最后执行分类任务。提出的模型IDSMLA采用额外树(ET)套袋集成技术。所提出的模型IDSMLA的性能使用精度和f1分数进行测量,使用10倍交叉验证。实验结果表明,所提出的模型IDSMLA大大优于不同的基于单分类器的模型、不同的集成模型以及文献中的不同模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intrusion Detection System using Ensemble Learning Analytics
An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.
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