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Faking Elephant Flows on the Count Min Sketch

机译:伪造大象在钟表中流动

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摘要

Detecting flows that have a large number of packets is an important function in network monitoring. Those flows known as elephants are typically few but account for a significant fraction of the traffic and it may be interesting to treat them differently. In high-speed networks, the number of flows can be very large and it is not practical to keep a list of the active flows. Instead, approximate data structures commonly referred to as sketches are used to detect elephant flows. The use of sketches significantly reduces the memory and computational effort needed to detect elephants at the cost of small inaccuracies in the detection. For example, the Count-Min Sketch (CMS) is widely used to estimate the frequency of elements in a set in general and the number of packets per flow in particular. However, the use of sketches also opens the door for an attacker to try to make that a specific flow that is not an elephant is detected as such. In this letter, an algorithm to perform such an attack on a Count-Min Sketch is presented and evaluated. The analysis and simulation results show that the attacker can create fake elephants even when he does not have any knowledge of how the Count-Min Sketch is implemented.
机译:检测具有大量数据包的流是网络监视的重要功能。那些称为大象的流量通常很少,但占交通的大部分,并且可能有趣地对待它们可能是有趣的。在高速网络中,流量的数量可能非常大,保留有效流列表是不实际的。相反,通常称为草图的近似数据结构用于检测大象流。使用草图显着降低了检测在检测中小不准确成本的大象所需的记忆和计算工作。例如,COUNT-MIN草图(CMS)广泛用于估计一般的集合中的元素的频率和每个流量的分组的数量。然而,使用草图也打开了攻击者的门,以试图使得没有检测到大象的特定流量。在这封信中,提出并评估了在计数初图草图上执行此类攻击的算法。分析和仿真结果表明,即使他没有任何知识,攻击者也可以创造假大象。

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