一种分层的基于豆子的异常检测模型

B. Tian, K. Merrick, Shui Yu, Jiankun Hu
{"title":"一种分层的基于豆子的异常检测模型","authors":"B. Tian, K. Merrick, Shui Yu, Jiankun Hu","doi":"10.1109/ICCNC.2013.6504158","DOIUrl":null,"url":null,"abstract":"A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.","PeriodicalId":229123,"journal":{"name":"2013 International Conference on Computing, Networking and Communications (ICNC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A hierarchical pea-based anomaly detection model\",\"authors\":\"B. Tian, K. Merrick, Shui Yu, Jiankun Hu\",\"doi\":\"10.1109/ICCNC.2013.6504158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.\",\"PeriodicalId\":229123,\"journal\":{\"name\":\"2013 International Conference on Computing, Networking and Communications (ICNC)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Computing, Networking and Communications (ICNC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCNC.2013.6504158\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Computing, Networking and Communications (ICNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCNC.2013.6504158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

提出了一种分层入侵检测模型来检测异常攻击和误用攻击。为了进一步加快训练和测试速度,采用基于pca的特征提取算法对数据进行降维处理。采用基于pca的算法过滤掉上层的正常数据。实验结果表明,PCA能够有效地去除原始数据集中的噪声,并且基于PCA的算法能够达到理想的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hierarchical pea-based anomaly detection model
A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.
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