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SVM Approach with CTNT to Detect DDoS Attacks in Grid Computing

机译:利用CTNT的SVM方法检测网格计算中的DDoS攻击

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摘要

In the last several years, DDoS attack methods become more sophisticated and effective. Hence, it is more difficult to detect the DDoS attack. In order to cope with these problems, there have been many researches on DDoS detection mechanism. However, the common shortcoming of the previous detection mechanisms is that they cannot detect new attacks. In this paper, we propose a new DDoS detection model based on Support Vector Machine (SVM). The proposed model uses SVM to automatically detect new DDoS attacks and uses Concentration Tendency of Network Traffic (CTNT) to analyze the characteristics of network traffic for DDoS attacks. Experimental results show that the proposed model can be a highly useful to detect various DDoS attacks.
机译:在最近几年中,DDoS攻击方法变得更加复杂和有效。因此,检测DDoS攻击更加困难。为了解决这些问题,对DDoS检测机制进行了很​​多研究。但是,以前的检测机制的共同缺点是它们无法检测到新的攻击。本文提出了一种基于支持向量机(SVM)的新型DDoS检测模型。所提出的模型使用SVM自动检测新的DDoS攻击,并使用网络流量集中趋势(CTNT)分析DDoS攻击的网络流量特征。实验结果表明,该模型对于检测各种DDoS攻击非常有用。

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