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    • 61. 发明授权
    • System and methods for anomaly detection and adaptive learning
    • 异常检测和自适应学习的系统和方法
    • US07424619B1
    • 2008-09-09
    • US10269694
    • 2002-10-11
    • Wei FanSalvatore J. Stolfo
    • Wei FanSalvatore J. Stolfo
    • G06F11/30G06F11/00G06F17/30H04L9/32H04L12/56H04L12/26H04L12/24G05B13/02
    • H04L63/1425G06F21/552H04L41/16H04L43/00
    • In a method of generating an anomaly detection model for classifying activities of a computer system, using a training set of data corresponding to activity on the computer system, the training set comprising a plurality of instances of data having features, and wherein each feature in said plurality of features has a plurality of values. For a selected feature and a selected value of the selected feature, a quantity is determined which corresponds to the relative sparsity of such value. The quantity may correspond to the difference between the number occurrences of the selected value and the number of occurrences of the most frequently occurring value. These instances are classified as anomaly and added to the training set of normal data to generate a rule set or other detection model.
    • 在产生用于对计算机系统的活动进行分类的异常检测模型的方法中,使用与计算机系统上的活动相对应的数据的训练集合,所述训练集合包括具有特征的多个数据实例,并且其中所述 多个特征具有多个值。 对于所选特征和所选特征的选定值,确定与该值相对稀疏度对应的数量。 数量可以对应于所选值的出现次数与最常发生值的出现次数之间的差异。 这些实例被分类为异常,并添加到正常数据的训练集中以生成规则集或其他检测模型。