Outlier detection methods are now used extensively, particularly in systems for detecting internal intrusions, in medicine, and in systems for detecting extremism in public political discussions on forums and social media. The aim of this work is to consider a fuzzy method of detecting outliers, based on elliptic clustering in the higher-dimensional space of attributes and using the Mahalanobis metrics for calculating the distances between objects and the center of a cluster. A procedure developed by the authors is used to find the optimum values of metaparameters of this algorithm. The classification of both individual events and complete sessions of user activity is considered, using an algorithm based on Welch’s t-statistics. The proposed procedures display a high quality of operation in solving two important problems of the stream analysis of complex data structures: the authentication of users by keystroke dynamics, and detecting extremist information in web text messages.

中文翻译：

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复杂结构事件流中的异常值检测

现在，异常检测方法已被广泛使用，尤其是在检测内部入侵的系统，医学以及在论坛和社交媒体上进行公开政治讨论的检测极端主义的系统中。这项工作的目的是考虑一种基于属性高维空间中的椭圆聚类并使用Mahalanobis度量来计算对象与聚类中心之间距离的模糊方法，用于检测离群值。作者开发的程序用于找到该算法的元参数的最佳值。使用基于Welch's t的算法，对单个事件和用户活动的完整会话进行分类-统计。所提出的过程在解决复杂数据结构的流分析的两个重要问题时显示出很高的操作质量：通过击键动力学对用户进行身份验证以及在Web文本消息中检测极端信息。