A blind detection method for tracing the real source of DDoS attack packets by cluster matching

Yonghong Chen, Xin Chen, H. Tian, Tian Wang, Yiqiao Cai
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引用次数: 2

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.
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
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。
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