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Streaming algorithms for robust, real-time detection of DDoS attacks

机译:流算法,用于可靠,实时地检测DDoS攻击

摘要

A distinct-count estimate is obtained in a guaranteed small footprint using a two level hash, distinct count sketch. A first hash fills the first-level hash buckets with an exponentially decreasing number of data-elements. These are then uniformly hashed to an array of second-level-hash tables, and have an associated total-element counter and bit-location counters. These counters are used to identify singletons and so provide a distinct-sample and a distinct-count. An estimate of the total distinct-count is obtained by dividing by the distinct-count by the probability of mapping a data-element to that bucket. An estimate of the total distinct-source frequencies of destination address can be found in a similar fashion. By further associating the distinct-count sketch with a list of singletons, a total singleton count and a heap containing the destination addresses ordered by their distinct-source frequencies, a tracking distinct-count sketch may be formed that has considerably improved query time.
机译:使用二级散列,非重复计数草图以保证的小占用空间获得非重复计数估计。第一散列用指数级减少的数据元素数量填充第一级散列桶。然后将它们统一哈希到第二级哈希表的数组,并具有关联的总元素计数器和位位置计数器。这些计数器用于识别单身人士,因此提供了不重复样本和不重复计数。通过将非重复计数除以将数据元素映射到该存储桶的概率,可以得到总非重复计数的估计。可以类似的方式找到目标地址的总不同源频率的估计值。通过进一步将非重复计数草绘与单例列表,总单例计数和包含按其非重复源频率排序的目标地址的堆相关联,可以形成跟踪非重复计数草绘,该草稿显着改善了查询时间。

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