高维网络流量数据中变点的分布式检测/定位
Distributed detection/localization of change-points in high-dimensional network traffic data
摘要：
我们提出了一种新的方法，分布式统计检测的变化点在大容量的网络流量。我们认为更具体的检测和识别分布式拒绝服务（DDoS）攻击的目标的任务。该算法称为DTopRank，通过聚集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。为了解决海量数据，同时限制网络内的通信开销，该方法结合了记录过滤在监视器级和一个非参数秩检验的双重删失时间序列在中央决策站点。的DTopRank算法的性能都说明了合成数据，以及从一个主要的互联网服务提供商提供的流量跟踪。
英文摘要：
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 nonparametric 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.
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