基于粗糙集和随机森林的入侵检测模型

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS

International Journal of Grid and High Performance Computing Pub Date : 2022-01-01 DOI:10.4018/ijghpc.301581

Ling Zhang, Jian-Wei Zhang, Nai Mei Fan, Hao Hao Zhao

{"title":"基于粗糙集和随机森林的入侵检测模型","authors":"Ling Zhang, Jian-Wei Zhang, Nai Mei Fan, Hao Hao Zhao","doi":"10.4018/ijghpc.301581","DOIUrl":null,"url":null,"abstract":"Currently, redundant data affects the speed of intrusion detection, many intrusion detection systems (IDS) have low detection rates and high false alert rate. Focusing on these weakness, a new intrusion detection model based on rough set and random forest (RSRFID) is designed. In the intrusion detection model, rough set (RS) is used to reduce the dimension of redundant attributes; the algorithm of decision tree(DT) is improved; a random forest (RF) algorithm based on attribute significances is proposed. Finally, the simulation experiment is given on NSL-KDD and UNSW-NB15 dataset. The results show: attributes of different types of datasets are reduced using RS; the detection rate of NSL-KDD is 93.73%, the false alert rate is 1.02%; the detection rate of NSL-KDD is 98.92%, the false alert rate is 2.92%.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"40 1","pages":"1-13"},"PeriodicalIF":0.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intrusion Detection Model Based on Rough Set and Random Forest\",\"authors\":\"Ling Zhang, Jian-Wei Zhang, Nai Mei Fan, Hao Hao Zhao\",\"doi\":\"10.4018/ijghpc.301581\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently, redundant data affects the speed of intrusion detection, many intrusion detection systems (IDS) have low detection rates and high false alert rate. Focusing on these weakness, a new intrusion detection model based on rough set and random forest (RSRFID) is designed. In the intrusion detection model, rough set (RS) is used to reduce the dimension of redundant attributes; the algorithm of decision tree(DT) is improved; a random forest (RF) algorithm based on attribute significances is proposed. Finally, the simulation experiment is given on NSL-KDD and UNSW-NB15 dataset. The results show: attributes of different types of datasets are reduced using RS; the detection rate of NSL-KDD is 93.73%, the false alert rate is 1.02%; the detection rate of NSL-KDD is 98.92%, the false alert rate is 2.92%.\",\"PeriodicalId\":43565,\"journal\":{\"name\":\"International Journal of Grid and High Performance Computing\",\"volume\":\"40 1\",\"pages\":\"1-13\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Grid and High Performance Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijghpc.301581\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Grid and High Performance Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijghpc.301581","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 0

摘要

目前，数据冗余影响了入侵检测的速度，许多入侵检测系统存在检测率低、误报率高的问题。针对这些缺点，设计了一种新的基于粗糙集和随机森林(RSRFID)的入侵检测模型。在入侵检测模型中，采用粗糙集(RS)对冗余属性进行降维;改进了决策树(DT)算法;提出了一种基于属性重要度的随机森林算法。最后，在NSL-KDD和UNSW-NB15数据集上进行了模拟实验。结果表明:利用RS对不同类型数据集的属性进行了约简;NSL-KDD的检出率为93.73%，误报率为1.02%;NSL-KDD的检出率为98.92%，误报率为2.92%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Intrusion Detection Model Based on Rough Set and Random Forest

Currently, redundant data affects the speed of intrusion detection, many intrusion detection systems (IDS) have low detection rates and high false alert rate. Focusing on these weakness, a new intrusion detection model based on rough set and random forest (RSRFID) is designed. In the intrusion detection model, rough set (RS) is used to reduce the dimension of redundant attributes; the algorithm of decision tree(DT) is improved; a random forest (RF) algorithm based on attribute significances is proposed. Finally, the simulation experiment is given on NSL-KDD and UNSW-NB15 dataset. The results show: attributes of different types of datasets are reduced using RS; the detection rate of NSL-KDD is 93.73%, the false alert rate is 1.02%; the detection rate of NSL-KDD is 98.92%, the false alert rate is 2.92%.

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

International Journal of Grid and High Performance Computing COMPUTER SCIENCE, THEORY & METHODS-

CiteScore

1.70

自引率

10.00%

发文量