一种通过集群匹配跟踪DDoS攻击报文真实来源的盲检测方法

Yonghong Chen, Xin Chen, H. Tian, Tian Wang, Yiqiao Cai
{"title":"一种通过集群匹配跟踪DDoS攻击报文真实来源的盲检测方法","authors":"Yonghong Chen, Xin Chen, H. Tian, Tian Wang, Yiqiao Cai","doi":"10.1109/ICCSN.2016.7586583","DOIUrl":null,"url":null,"abstract":"With the rapid growth of the Internet, the impact of attacks becomes more serious. IP spoofing makes hosts hard to defend against DDoS attacks. In this paper, we propose a blind detection method for tracing the real source of DDoS attack packets. Tracing the real source of a single-packet is difficult, so we trace-back a cluster of similar packets rather than a single-packet by cluster matching. We choose K-harmonic means clustering method to preprocess the packets according to our proposed quantitative model, at the same time, we propose an approach to determine the best number of clusters. In addition, we propose a novel detection algorithm about cluster matching for tracing the real source of packet clusters based on K-harmonic means and our improved silhouette. Experimental results show that our method can detect the real source of packets with up to 92.54% accuracy.","PeriodicalId":158877,"journal":{"name":"2016 8th IEEE International Conference on Communication Software and Networks (ICCSN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A blind detection method for tracing the real source of DDoS attack packets by cluster matching\",\"authors\":\"Yonghong Chen, Xin Chen, H. Tian, Tian Wang, Yiqiao Cai\",\"doi\":\"10.1109/ICCSN.2016.7586583\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid growth of the Internet, the impact of attacks becomes more serious. IP spoofing makes hosts hard to defend against DDoS attacks. In this paper, we propose a blind detection method for tracing the real source of DDoS attack packets. Tracing the real source of a single-packet is difficult, so we trace-back a cluster of similar packets rather than a single-packet by cluster matching. We choose K-harmonic means clustering method to preprocess the packets according to our proposed quantitative model, at the same time, we propose an approach to determine the best number of clusters. In addition, we propose a novel detection algorithm about cluster matching for tracing the real source of packet clusters based on K-harmonic means and our improved silhouette. Experimental results show that our method can detect the real source of packets with up to 92.54% accuracy.\",\"PeriodicalId\":158877,\"journal\":{\"name\":\"2016 8th IEEE International Conference on Communication Software and Networks (ICCSN)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 8th IEEE International Conference on Communication Software and Networks (ICCSN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSN.2016.7586583\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 8th IEEE International Conference on Communication Software and Networks (ICCSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSN.2016.7586583","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

随着互联网的快速发展,网络攻击的影响越来越严重。IP欺骗使主机难以抵御DDoS攻击。在本文中,我们提出了一种盲检测方法来追踪DDoS攻击数据包的真实来源。跟踪单个数据包的真实来源是困难的,因此我们通过集群匹配来跟踪类似数据包的集群,而不是单个数据包。根据所提出的定量模型,选择k调和均值聚类方法对数据包进行预处理,同时提出了一种确定最佳聚类数的方法。此外,我们提出了一种新的基于k谐波均值和改进轮廓的聚类匹配检测算法,用于跟踪数据包聚类的真实来源。实验结果表明,该方法能够检测出数据包的真实来源,准确率高达92.54%。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A blind detection method for tracing the real source of DDoS attack packets by cluster matching
With the rapid growth of the Internet, the impact of attacks becomes more serious. IP spoofing makes hosts hard to defend against DDoS attacks. In this paper, we propose a blind detection method for tracing the real source of DDoS attack packets. Tracing the real source of a single-packet is difficult, so we trace-back a cluster of similar packets rather than a single-packet by cluster matching. We choose K-harmonic means clustering method to preprocess the packets according to our proposed quantitative model, at the same time, we propose an approach to determine the best number of clusters. In addition, we propose a novel detection algorithm about cluster matching for tracing the real source of packet clusters based on K-harmonic means and our improved silhouette. Experimental results show that our method can detect the real source of packets with up to 92.54% accuracy.
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
期刊最新文献
Detecting sports fatigue from speech by support vector machine Error beacon filtering algorithm based on K-means clustering for underwater Wireless Sensor Networks Transmit beamforming optimization for energy efficiency maximization in downlink distributed antenna systems Research of 3D face recognition algorithm based on deep learning stacked denoising autoencoder theory Improved propagator method for joint angle and Doppler estimation based on structured least squares
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1