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Proving Optimality of DWS (Distance-Weighted Sampling) Probability Function for FMS IP Trace-Back Technique

机译:证明FMS IP回溯技术的DWS(距离加权采样)概率函数的最优性

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A convergence time is the time to construct the attack path. In FMS, each router sends its IP by fragments, and the victim should wait until the last router sends its last IP fragment. Therefore, the convergence time is determined by the slowest router. Kim et al.[1] proposed a new sampling theory, so called Distance-Weighted Sampling that did not penalize the furthest router. They showed that the sampling probability p = f(d) where the f(d) is a decreasing function of distance d traveled by the target packet. Since the convergence time will be determined by the slowest router, we have to maximize the minimum number of IP fragments incoming at each router station. The optimal choice was stated as f(d) =1/(2(d+1)) sample simulation study supported their claim. In this article we are going to prove rigorously that 1/(2(d+1)) is indeed the optimal sampling probability under mild assumptions.
机译:收敛时间是构建攻击路径的时间。在FMS中,每个路由器都按分段发送其IP,而受害者应等到最后一个路由器发送其最后的IP分段。因此,收敛时间由最慢的路由器确定。 Kim等[1]提出了一种新的采样理论,即所谓的距离加权采样,它不会惩罚最远的路由器。他们表明采样概率p = f(d),其中f(d)是目标数据包经过的距离d的递减函数。由于收敛时间将由最慢的路由器确定,因此我们必须最大化每个路由器站上传入的IP片段的最小数量。最佳选择表示为f(d)= 1 /(2(d + 1))样本仿真研究支持了他们的主张。在本文中,我们将严格证明1 /(2(d + 1))实际上是在温和假设下的最佳采样概率。

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