高维网络流量数据中变点的检测与定位
Detection and localization of change-points in high-dimensional network traffic data
摘要：
我们提出了一种新的和有效的方法，我们将在下面的文件中调用TopRank，用于检测高维数据中的变点。这个问题越来越受到网络安全界的关注，因为拒绝服务（DoS）攻击等网络异常会导致Internet流量发生变化。我们的方法包括一个数据减少阶段的基础上记录过滤，其次是一个非参数的变点检测测试的基础上$U$统计。使用这种方法，我们可以处理大量的数据流，并在运行中执行异常检测和定位。我们展示了它是如何适用于一些真正的互联网流量提供的法国电信（法国互联网服务提供商）的ANR-RNRT OSCAR项目的框架。这种方法是非常有吸引力的，因为它受益于低计算负载，并能够检测和定位几种类型的网络异常。我们还评估了TopRank算法使用合成数据的性能，并将其与基于随机聚合的替代方法进行比较。
英文摘要：
We propose a novel and efficient method, that we shall call TopRank in the following paper, for detecting change-points in high-dimensional data. This issue is of growing concern to the network security community since network anomalies such as Denial of Service (DoS) attacks lead to changes in Internet traffic. Our method consists of a data reduction stage based on record filtering, followed by a nonparametric change-point detection test based on $U$-statistics. Using this approach, we can address massive data streams and perform anomaly detection and localization on the fly. We show how it applies to some real Internet traffic provided by France-Télécom (a French Internet service provider) in the framework of the ANR-RNRT OSCAR project. This approach is very attractive since it benefits from a low computational load and is able to detect and localize several types of network anomalies. We also assess the performance of the TopRank algorithm using synthetic data and compare it with alternative approaches based on random aggregation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
