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Collaborative detection and filtering of shrew DDoS attacks using spectral analysis

机译:利用频谱分析对DDoS攻击进行协同检测和过滤

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This paper presents a new spectral template-matching approach to countering shrew distributed denial-of-service (DDoS) attacks. These attacks are stealthy, periodic, pulsing, and low-rate in attack volume, very different from the flooding type of attacks. They are launched with high narrow spikes in very low frequency, periodically. Thus, shrew attacks may endanger the victim systems for a long time without being detected. In other words, such attacks may reduce the quality of services unnoticeably. Our defense method calls for collaborative detection and filtering (CDF) of shrew DDoS attacks. We detect shrew attack flows hidden in legitimate TCP/UDP streams by spectral analysis against pre-stored template of average attack spectral characteristics. This novel scheme is suitable for either software or hardware implementation.
机译:本文提出了一种新的频谱模板匹配方法,以应对sh式分布式拒绝服务(DDoS)攻击。这些攻击具有隐身性,周期性,脉冲性和低攻击率,与泛洪型攻击非常不同。它们以非常低的频率周期性地以高窄尖峰发射。因此,猛烈攻击可能会长时间威胁受害系统而不会被发现。换句话说,此类攻击可能会明显降低服务质量。我们的防御方法要求对机密DDoS攻击进行协作检测和过滤(CDF)。通过对平均攻击频谱特征的预存模板进行频谱分析,我们可以检测出隐藏在合法TCP / UDP流中的泼妇攻击流。这种新颖的方案适用于软件或硬件实现。

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