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    • 1. 发明申请
    • Systems and Methods for Dynamic Anomaly Detection
    • 动态异常检测系统与方法
    • US20090234899A1
    • 2009-09-17
    • US12046394
    • 2008-03-11
    • Stephen Patrick Kramer
    • Stephen Patrick Kramer
    • G06F7/544
    • G06F17/30539
    • Methods and systems for detecting anomalies in sets of data are disclosed, including: computing components of one or more types of feature vectors at a plurality of values of one or more independent variables, each type of the feature vectors characterizing a set of input data being dependent on the one or more independent variables; computing one or more types of output values corresponding to each type of feature vectors as a function of the one or more independent variables using a nonlinear sequence analysis method; and detecting anomalies in how the one or more types of output values change as functions of the one or more independent variables.
    • 公开了一种用于检测数据集中的异常的方法和系统,包括:以一个或多个独立变量的多个值计算一种或多种类型的特征向量的分量,每种类型的特征向量表征一组输入数据, 依赖于一个或多个独立变量; 使用非线性序列分析方法来计算与每种类型的特征向量相对应的一种或多种类型的输出值作为所述一个或多个独立变量的函数; 以及检测所述一个或多个类型的输出值如何随着所述一个或多个独立变量的函数而变化的异常。
    • 3. 发明授权
    • Systems and methods for dynamic anomaly detection
    • 动态异常检测的系统和方法
    • US08738652B2
    • 2014-05-27
    • US12046394
    • 2008-03-11
    • Stephen Patrick Kramer
    • Stephen Patrick Kramer
    • G06F17/10
    • G06F17/30539
    • Methods and systems for detecting anomalies in sets of data are disclosed, including: computing components of one or more types of feature vectors at a plurality of values of one or more independent variables, each type of the feature vectors characterizing a set of input data being dependent on the one or more independent variables; computing one or more types of output values corresponding to each type of feature vectors as a function of the one or more independent variables using a nonlinear sequence analysis method; and detecting anomalies in how the one or more types of output values change as functions of the one or more independent variables.
    • 公开了一种用于检测数据集中的异常的方法和系统,包括:以一个或多个独立变量的多个值计算一种或多种类型的特征向量的分量,每种类型的特征向量表征一组输入数据, 依赖于一个或多个独立变量; 使用非线性序列分析方法来计算与每种类型的特征向量相对应的一种或多种类型的输出值作为所述一个或多个独立变量的函数; 以及检测所述一个或多个类型的输出值如何随着所述一个或多个独立变量的函数而变化的异常。