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1. 发明授权

US07941382B2 Method of classifying and active learning that ranks entries based on multiple scores, presents entries to human analysts, and detects and/or prevents malicious behavior 有权
标题翻译：基于多个分数对条目进行分类和主动学习的方法，向人类分析人员提供条目，并检测和/或防止恶意行为
公开(公告)号：US07941382B2
公开(公告)日：2011-05-10
申请号：US11871587
申请日：2007-10-12
申请人： Jack W. Stokes , John C. Platt , Michael Shilman , Joseph L. Kravis
发明人： Jack W. Stokes , John C. Platt , Michael Shilman , Joseph L. Kravis
IPC分类号： G06E1/00
CPC分类号： G06F15/16
摘要： A malicious behavior detection/prevention system, such as an intrusion detection system, is provided that uses active learning to classify entries into multiple classes. A single entry can correspond to either the occurrence of one or more events or the non-occurrence of one or more events. During a training phase, entries are automatically classified into one of multiple classes. After classifying the entry, a generated model for the determined class is utilized to determine how well an entry corresponds to the model. Ambiguous classifications along with entries that do not fit the model well for the determined class are selected for labeling by a human analyst. The selected entries are presented to a human analyst for labeling. These labels are used to further train the classifier and the models. During an evaluation phase, entries are automatically classified using the trained classifier and a policy associated with determined class is applied.
摘要翻译：提供了一种恶意行为检测/预防系统，例如入侵检测系统，其使用主动学习将条目分类到多个类中。单个条目可以对应于一个或多个事件的发生或一个或多个事件的不发生。在训练阶段，条目自动分为多个类别之一。在对条目进行分类之后，使用所确定的类的生成模型来确定条目对应于模型的良好程度。选择不确定的分类以及不符合确定类别的模型的条目，由人类分析师进行标签。选定的条目提交给人类分析人员进行标签。这些标签用于进一步训练分类器和型号。在评估阶段，使用训练有素的分类器对条目进行自动分类，并应用与确定类相关联的策略。

2. 发明申请

US20090099988A1 ACTIVE LEARNING USING A DISCRIMINATIVE CLASSIFIER AND A GENERATIVE MODEL TO DETECT AND/OR PREVENT MALICIOUS BEHAVIOR 有权
标题翻译：主动学习使用分类分类器和生成模型来检测和/或防止恶意行为
公开(公告)号：US20090099988A1
公开(公告)日：2009-04-16
申请号：US11871587
申请日：2007-10-12
申请人： Jack W. Stokes , John C. Platt , Michael Shilman , Joseph L. Kravis
发明人： Jack W. Stokes , John C. Platt , Michael Shilman , Joseph L. Kravis
IPC分类号： G06F15/18
CPC分类号： G06F15/16
摘要： A malicious behavior detection/prevention system, such as an intrusion detection system, is provided that uses active learning to classify entries into multiple classes. A single entry can correspond to either the occurrence of one or more events or the non-occurrence of one or more events. During a training phase, entries are automatically classified into one of multiple classes. After classifying the entry, a generated model for the determined class is utilized to determine how well an entry corresponds to the model. Ambiguous classifications along with entries that do not fit the model well for the determined class are selected for labeling by a human analyst The selected entries are presented to a human analyst for labeling. These labels are used to further train the classifier and the models. During an evaluation phase, entries are automatically classified using the trained classifier and a policy associated with determined class is applied.
摘要翻译：提供了一种恶意行为检测/预防系统，例如入侵检测系统，其使用主动学习将条目分类到多个类中。单个条目可以对应于一个或多个事件的发生或一个或多个事件的不发生。在训练阶段，条目自动分为多个类别之一。在对条目进行分类之后，使用所确定的类的生成模型来确定条目对应于模型的良好程度。选择不确定的分类以及不符合确定类别的模型的条目，由人类分析人员进行标签。选定的条目将提交给人类分析人员进行标签。这些标签用于进一步训练分类器和型号。在评估阶段，使用训练有素的分类器对条目进行自动分类，并应用与确定类相关联的策略。

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