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An Improved Wavelet Analysis Method for Detecting DDoS Attacks

机译:一种改进的检测DDoS攻击的小波分析方法

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Wavelet Analysis method is considered as one of the most efficient methods for detecting DDoS attacks. However, during the peak data communication hours with a large amount of data transactions, this method is required to collect too many samples that will greatly increase the computational complexity. Therefore, the real-time response time as well as the accuracy of attack detection becomes very low. To address the above problem, we propose a new DDoS detection method called Modified Wavelet Analysis method which is based on the existing Isomap algorithm and wavelet analysis. In the paper, we present our new model and algorithm for detecting DDoS attacks and demonstrate the reasons of why we enlarge the Hurstȁ9;s value of the self-similarity in our new approach. Finally we present an experimental evaluation to demonstrate that the proposed method is more efficient than the other traditional methods based on wavelet analysis.
机译:小波分析方法被认为是检测DDoS攻击的最有效方法之一。但是,在具有大量数据事务的高峰数据通信时间中,此方法需要收集太多样本,这将大大增加计算复杂性。因此,实时响应时间以及攻击检测的准确性变得非常低。为了解决上述问题,我们提出了一种新的DDoS检测方法,称为“改进小波分析”方法,该方法基于现有的Isomap算法和小波分析。在本文中,我们介绍了用于检测DDoS攻击的新模型和算法,并说明了为什么在我们的新方法中扩大Hurstȁ9的自相似性值的原因。最后,我们提供了一个实验评估,以证明该方法比基于小波分析的其他传统方法更有效。

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