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A Cluster-Based Context-Tree Model for Multivariate Data Streams with Applications to Anomaly Detection

机译:基于集群的多数据流上下文树模型及其在异常检测中的应用

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Many applications, such as telecommunication and commercial video broadcasting systems, computer and networks, and Web mining, require modeling data streams that exhibit context dependency. Context dependency refers to the fact that the statistical distribution of a new sample is heavily conditioned by a set of the most recent samples that precedes it. However, statistical models such as context trees (CTs) that capture context dependency tend to be poorly scalable. This paper proposes a solution to the scalability problem of these models by transforming a data stream into high-level aggregates of clusters instead of modeling the original data stream. Using an information-theoretical approach, we leverage existing clustering techniques for static categorical data sets to capture dynamic data streams based on the CT models. Because the proposed approach can be applied repeatedly on different levels of a clustering hierarchy, it is suitable for predicting trends and detecting anomalies at any aggregate (or detail) level required. Experimental results, including video stream modeling, network intrusion detection, and Monte Carlo simulations, show that the proposed method is efficient in capturing high-level aggregates of large-scale dynamic systems and very effective for trend prediction and anomaly detection.
机译:许多应用程序,例如电信和商业视频广播系统,计算机和网络以及Web挖掘,都需要对表现出上下文相关性的数据流进行建模。上下文相关性是指这样一个事实,即新样本的统计分布在很大程度上取决于之前的一组最新样本。但是,捕获上下文相关性的统计模型(例如上下文树(CT))往往扩展性较差。本文提出了通过将数据流转换为群集的高级聚合而不是对原始数据流进行建模的方法来解决这些模型的可伸缩性问题。使用信息理论方法,我们利用现有的聚类技术处理静态分类数据集,以基于CT模型捕获动态数据流。由于所提出的方法可以在聚类层次结构的不同级别上重复应用,因此适用于预测趋势并在所需的任何聚合(或详细信息)级别检测异常。包括视频流建模,网络入侵检测和蒙特卡洛模拟在内的实验结果表明,该方法可有效捕获大型动态系统的高级集合,并且对于趋势预测和异常检测非常有效。

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