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    • 11. 发明申请
    • REALTIME MULTIPLE ENGINE SELECTION AND COMBINING
    • 实时多发动机选择和组合
    • US20120084859A1
    • 2012-04-05
    • US12894185
    • 2010-09-30
    • Kira RadinskyRoy VarshavskyJack W. StokesVladimir HolostovEdward Schaefer
    • Kira RadinskyRoy VarshavskyJack W. StokesVladimir HolostovEdward Schaefer
    • G06F21/00G06F17/30
    • G06F21/563G06F21/56G06Q10/06G06Q30/00
    • Architecture that selects a classification engine based on the expertise of the engine to process a given entity (e.g., a file). Selection of an engine is based on a probability that the engine will detect an unknown entity classification using properties of the entity. One or more of the highest ranked engines are activated in order to achieve the desired performance. A statistical, performance-light module is employed to skip or select several performance-demanding processes. Methods and algorithms are utilized for learning based on matching the best classification engine(s) to detect the entity class based on the entity properties. A user selection option is provided for specifying a maximum number of ranked, classification engines to consider for each state of the machine. A user can also select the minimum probability of detection for a specific entity (e.g., unknown file). The best classifications are re-evaluated over time as the classification engines are updated.
    • 基于引擎的专长来选择分类引擎以处理给定实体(例如,文件)的架构。 引擎的选择是基于引擎将使用实体的属性来检测未知实体分类的概率。 一个或多个最高排名的引擎被激活以实现期望的性能。 采用统计的性能灯模块来跳过或选择若干性能要求高的过程。 基于匹配最佳分类引擎的方法和算法用于学习,以根据实体属性检测实体类。 提供用户选择选项,用于指定针对机器的每个状态考虑的排名最大的分类引擎。 用户还可以选择特定实体(例如,未知文件)的最小检测概率。 随着分类引擎的更新,最好的分类会随着时间的推移重新评估。
    • 13. 发明申请
    • CHAIN OF EVENTS TRACKING WITH DATA TAINTING FOR AUTOMATED SECURITY FEEDBACK
    • 用于自动安全反馈的数据跟踪事件链
    • US20090328210A1
    • 2009-12-31
    • US12165608
    • 2008-06-30
    • Vassilii KhachaturovVladimir HolostovJohn Neystadt
    • Vassilii KhachaturovVladimir HolostovJohn Neystadt
    • G06F21/00
    • G06F21/552
    • An automated security feedback arrangement is provided by which a specialized audit record called a tainting record is linked to data crossing the perimeter of a corpnet that comes from potentially untrusted sources. The linked tainting record operates to taint such data which may be received from external sources such as e-mail and websites or which may comprise data that is imported into the corpnet from mobile computing devices. Data that is derived from the original data is also tainted using a linked tainting record which includes a pointer back to the previous tainting record. The linking and pointing back are repeated for all subsequent derivations of data to thus create an audit trail that may be used to reconstruct the chain of events between the original data crossing the perimeter and any security compromise that may later be detected in the corpnet.
    • 提供了一种自动安全反馈安排,通过该安排,称为污染记录的专门审核记录与跨越可能不受信任的来源的公司的边界的数据相关联。 链接的污染记录用于污染可从诸如电子邮件和网站的外部来源接收的这些数据,或者可以包括从移动计算设备导入到该公司的数据。 从原始数据导出的数据也使用链接的污点记录来污染,该记录包括指向前一个污点记录的指针。 为了所有后续的数据导出重复链接和指向,从而创建可用于重建跨越周界的原始数据之间的事件链以及可能在公司网络中稍后被检测到的任何安全损害的审计跟踪。
    • 16. 发明授权
    • Realtime multiple engine selection and combining
    • 实时多引擎选择和组合
    • US08869277B2
    • 2014-10-21
    • US12894185
    • 2010-09-30
    • Kira RadinskyRoy VarshavskyJack W. StokesVladimir HolostovEdward Schaefer
    • Kira RadinskyRoy VarshavskyJack W. StokesVladimir HolostovEdward Schaefer
    • G06F21/00G06Q30/00G06F21/56
    • G06F21/563G06F21/56G06Q10/06G06Q30/00
    • Architecture that selects a classification engine based on the expertise of the engine to process a given entity (e.g., a file). Selection of an engine is based on a probability that the engine will detect an unknown entity classification using properties of the entity. One or more of the highest ranked engines are activated in order to achieve the desired performance. A statistical, performance-light module is employed to skip or select several performance-demanding processes. Methods and algorithms are utilized for learning based on matching the best classification engine(s) to detect the entity class based on the entity properties. A user selection option is provided for specifying a maximum number of ranked, classification engines to consider for each state of the machine. A user can also select the minimum probability of detection for a specific entity (e.g., unknown file). The best classifications are re-evaluated over time as the classification engines are updated.
    • 基于引擎的专长来选择分类引擎以处理给定实体(例如,文件)的架构。 引擎的选择是基于引擎将使用实体的属性来检测未知实体分类的概率。 一个或多个最高排名的引擎被激活以实现期望的性能。 采用统计的性能灯模块来跳过或选择若干性能要求高的过程。 基于匹配最佳分类引擎的方法和算法用于学习,以根据实体属性检测实体类。 提供用户选择选项,用于指定针对机器的每个状态考虑的排名最大的分类引擎。 用户还可以选择特定实体(例如,未知文件)的最小检测概率。 随着分类引擎的更新,最好的分类会随着时间的推移重新评估。