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Distributed detection/localization of change-points in high-dimensional network traffic data

机译：高维网络流量数据中变化点的分布式检测/定位

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摘要

We propose a novel approach for distributed statistical detection of change-points in high-volume network traffic. We consider more specifically the task of detecting and identifying the targets of Distributed Denial of Service (DDoS) attacks. The proposed algorithm, called DTopRank, performs distributed network anomaly detection by aggregating the partial information gathered in a set of network monitors. In order to address massive data while limiting the communication overhead within the network, the approach combines record filtering at the monitor level and a nonpara-metric rank test for doubly censored time series at the central decision site. The performance of the DTopRank algorithm is illustrated both on synthetic data as well as from a traffic trace provided by a major Internet service provider.
机译：我们提出了一种新颖的方法，用于在大容量网络流量中对变化点进行分布式统计检测。我们更具体地考虑检测和识别分布式拒绝服务（DDoS）攻击目标的任务。所提出的算法称为DTopRank，它通过汇总在一组网络监视器中收集的部分信息来执行分布式网络异常检测。为了处理海量数据，同时限制网络内的通信开销，该方法将监控器级别的记录过滤与中央决策站点的双重审查时间序列的非参数秩检验相结合。在综合数据以及主要互联网服务提供商提供的流量跟踪中都说明了DTopRank算法的性能。

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