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1. 发明授权

US09142106B2 Tailgating detection 有权
标题翻译：尾部检测
公开(公告)号：US09142106B2
公开(公告)日：2015-09-22
申请号：US13478708
申请日：2012-05-23
申请人： Eva Jerhotova , Valerie Guralnik
发明人： Eva Jerhotova , Valerie Guralnik
IPC分类号： G08B13/00 , G08B29/18 , G08B13/08 , G08B13/196
CPC分类号： G08B13/00 , G08B13/08 , G08B13/19613 , G08B29/185
摘要： Methods, systems, and computer-readable media for tailgating detection are described herein. One method includes collecting, via a computing device, access log data associated with a profile; processing, by a processor coupled to the computing device, the access log data to obtain a statistical access model; detecting, by the processor, a tailgating sequence based on the statistical access model; and providing, by the processor, a notification that the tailgating sequence has occurred.
摘要翻译：本文描述了用于尾随检测的方法，系统和计算机可读介质。一种方法包括经由计算设备收集与简档相关联的访问日志数据; 通过耦合到所述计算设备的处理器处理所述访问日志数据以获得统计访问模型; 由处理器基于统计访问模型检测尾随序列; 以及由所述处理器提供所述尾随序列已发生的通知。

2. 发明授权

US08680995B2 Access control system based upon behavioral patterns 有权
标题翻译：基于行为模式的访问控制系统
公开(公告)号：US08680995B2
公开(公告)日：2014-03-25
申请号：US12695542
申请日：2010-01-28
申请人： Ashwin G , Santhanakrishnan Ponnambalam , Sriram Subramanian , Sivakumar Balakrishnan , Valerie Guralnik , Walt Heimerdinger
发明人： Ashwin G , Santhanakrishnan Ponnambalam , Sriram Subramanian , Sivakumar Balakrishnan , Valerie Guralnik , Walt Heimerdinger
IPC分类号： G08B13/00 , G05B19/00 , G08B29/00 , G08B25/00 , G06K9/00 , G06F15/173 , H04L29/06 , G06F21/00
CPC分类号： G07C9/00126
摘要： A method and apparatus for detecting behavioral changes in a security system is provided. The method includes the steps of providing a secured area having a plurality of security zones where access to each is controlled by an access controller, detecting entrances to at least some of the plurality of security zones by an authorized person through respective access controllers of the plurality of zones over a predetermined previous time period, forming a probability model of entry into each of the plurality of security zones from the detected entrances over the previous time period, detecting access requests for the authorized user from the access controllers during a current time period, and generating a security alert upon determining that an access request of the current access requests exceeds a probability threshold value associated with the probability model.
摘要翻译：提供一种用于检测安全系统中的行为变化的方法和装置。该方法包括以下步骤：提供具有多个安全区域的安全区域，其中对每个安全区域的访问由访问控制器控制，由授权人员通过多个访问控制器的各个访问控制器检测至少一些安全区域的入口在预定的上一时间段内的区域形成从所检测的入口处在前一时间段内进入多个安全区域中的每一个的概率模型，在当前时间段期间检测来自访问控制器的授权用户的访问请求，以及在确定当前访问请求的访问请求超过与概率模型相关联的概率阈值时，生成安全警报。

3. 发明申请

US20120330611A1 MONITORING ACCESS TO A LOCATION 有权
标题翻译：监控访问位置
公开(公告)号：US20120330611A1
公开(公告)日：2012-12-27
申请号：US13166178
申请日：2011-06-22
申请人： Vit Libal , Valerie Guralnik
发明人： Vit Libal , Valerie Guralnik
IPC分类号： G06F17/18
CPC分类号： G06F21/45 , G06F9/542 , G07C9/00 , G07C2209/08 , G08B31/00
摘要： Devices, methods, and systems for monitoring access to a location are described herein. One or more method embodiments include determining data associated with an access event associated with a location, determining whether the access event is an anomalous access event using the data associated with the access event and a statistical model of data associated with a number of non-anomalous access events associated with the location, and assessing, if the access event is determined to be an anomalous access event, the anomalous access event. In various embodiments, assessing the anomalous access event includes at least one of determining an anomaly type associated with the anomalous access event, determining an anomaly classification confidence associated with the anomalous access event, determining an anomaly severity associated with the anomalous access event, and determining a reliability associated with the statistical model.
摘要翻译：这里描述了用于监视对位置的访问的设备，方法和系统。一个或多个方法实施例包括确定与与位置相关联的访问事件相关联的数据，使用与访问事件相关联的数据来确定访问事件是否是异常访问事件，以及与多个非异常的数据相关联的数据的统计模型访问与所述位置相关联的事件，以及如果所述访问事件被确定为异常访问事件，则评估所述异常访问事件。在各种实施例中，评估异常访问事件包括确定与异常访问事件相关联的异常类型中的至少一个，确定与异常访问事件相关联的异常分类置信度，确定与异常访问事件相关联的异常严重程度，以及确定与统计模型相关的可靠性。

4. 发明申请

US20120005208A1 SYSTEM FOR INFORMATION DISCOVERY IN VIDEO-BASED DATA 有权
标题翻译：基于视频数据的信息发现系统
公开(公告)号：US20120005208A1
公开(公告)日：2012-01-05
申请号：US12829725
申请日：2010-07-02
申请人： Valerie Guralnik , Kirk Schloegel , Petr Cisar
发明人： Valerie Guralnik , Kirk Schloegel , Petr Cisar
IPC分类号： G06F17/30 , G06F7/00
CPC分类号： G06F17/30781
摘要： A system for information discovery of items, such as individuals or objects, from video-based tracks. The system may compute similarities of characteristics of the items and present the results in a matrix form. A similarity portrayal may have nodes representing the items with edges between the nodes. The edges may have weights in the form of vectors indicating similarities of the characteristics between the nodes situated at the ends of the edges. The edges may be augmented with temporal and spatial properties from the tracks which cover the items. These properties may play a part in a multi-objective presentation of information about the items in terms of a negative or supportive basis. The presentation may be partitioned into clusters which may lead to a merger of items or tracks. The system may pave a way for higher-level information discovery such as video-based social networks.
摘要翻译：用于信息发现的系统，用于从基于视频的轨道发现项目，例如个人或对象。系统可以计算项目的特征的相似性并以矩阵形式呈现结果。相似性描绘可以具有表示具有节点之间的边的项的节点。边缘可以具有向量形式的权重，指示位于边缘端部处的节点之间的特征的相似性。可以从覆盖物品的轨道的时间和空间属性来增加边缘。这些属性可能在以负面或支持为基础的多目标呈现关于项目的信息中发挥作用。呈现可以被划分成可以导致项目或轨道的合并的群集。该系统可能为更高级别的信息发现铺平道路，例如基于视频的社交网络。

5. 发明授权

US07533070B2 Automatic fault classification for model-based process monitoring 有权
标题翻译：基于模型的过程监控的自动故障分类
公开(公告)号：US07533070B2
公开(公告)日：2009-05-12
申请号：US11442857
申请日：2006-05-30
申请人： Valerie Guralnik , Wendy K. Foslien
发明人： Valerie Guralnik , Wendy K. Foslien
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00
CPC分类号： G05B23/0254 , G05B23/0281 , G06K9/622
摘要： A computer implemented method, system and program product for automatic fault classification. A set of abnormal data can be automatically grouped based on sensor contribution to a prediction error. A principal component analysis (PCA) model of normal behavior can then be applied to a set of newly generated data, in response to automatically grouping the set of abnormal data based on the sensor contribution to the prediction error. Data points can then be identified, which are indicative of abnormal behavior. Such an identification step can occur in response to applying the principal component analysis mode of normal behavior to the set of newly generated data in order to cluster and classify the data points in order to automatically classify one or more faults thereof. The data points are automatically clustered, in order to identify a set of similar events, in response to identifying the data points indicative of abnormal behavior.
摘要翻译：一种用于自动故障分类的计算机实现方法，系统和程序产品。可以根据对预测误差的传感器贡献自动分组一组异常数据。因此，正常行为的主成分分析（PCA）模型可以应用于一组新生成的数据，以响应于基于传感器对预测误差的贡献自动地对该组异常数据进行分组。然后可以识别数据点，这表示异常行为。这样的识别步骤可以响应于将正常行为的主成分分析模式应用于新生成的数据集，以便对数据点进行聚类和分类，以便自动分类其一个或多个故障。响应于识别指示异常行为的数据点，数据点被自动聚类，以便识别一组类似的事件。

6. 发明申请

US20080294374A1 PRINCIPAL COMPONENT ANALYSIS BASED FAULT CLASSIFICATION 有权
标题翻译：基于主成分分析的故障分类
公开(公告)号：US20080294374A1
公开(公告)日：2008-11-27
申请号：US12187975
申请日：2008-08-07
申请人： Valerie Guralnik , Wendy K. Foslien
发明人： Valerie Guralnik , Wendy K. Foslien
IPC分类号： G06F17/18
CPC分类号： G06K9/622 , G05B23/0254 , G06K9/6284
摘要： Principle Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events is identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.
摘要翻译：原理分量分析（PCA）用于对过程进行建模，采用聚类技术对基于PCA模型的传感器残差代表事件的偏移进行分组。 PCA模型对正常数据进行培训，然后运行包含正常数据的历史数据和包含事件的数据。事件的不良演员数据通过来自正常模型的Q（残差）和T2（异常方差）统计的偏移来识别，导致不良演员向量的时间序列。形成彼此类似的不良演员模式的群集，然后与事件相关联。

7. 发明授权

US07337086B2 System and method for combining diagnostic evidences for turbine engine fault detection 有权
标题翻译：用于组合涡轮发动机故障检测诊断证据的系统和方法
公开(公告)号：US07337086B2
公开(公告)日：2008-02-26
申请号：US11583248
申请日：2006-10-18
申请人： Valerie Guralnik , Dinkar Mylaraswamy , Harold C. Voges
发明人： Valerie Guralnik , Dinkar Mylaraswamy , Harold C. Voges
IPC分类号： G01M15/00
CPC分类号： G05B23/0262 , G05B23/0275
摘要： A system and method for combining conclusions from multiple fault detection techniques to isolate likely faults in a turbine engine is provided. The system and method provide the ability to effectively deal with multiple concurrent faults in the engine. Additionally, the embodiments of the invention provide the ability to correctly characterize multiple conclusions generated from evidence having different levels of interdependence. In one embodiment, the conclusions based on device data with high dependency are aggregated using a high dependency aggregation rule, and the resulting high-dependency sets are then further aggregated using a weak dependency rule. Finally, any conclusions based on independent evidence can be aggregated using an independent combination rule. The resulting aggregation determines which fault(s) are most likely indicated by the plurality of conclusions, taken into account the dependency of the device data used to generate the conclusions.
摘要翻译：提供了一种用于组合来自多个故障检测技术以分离涡轮发动机中的可能故障的结论的系统和方法。该系统和方法提供了有效处理引擎中多个并发故障的能力。另外，本发明的实施例提供了正确表征由具有不同相互依赖关系的证据产生的多个结论的能力。在一个实施例中，使用高依赖性聚合规则来聚合基于具有高依赖性的设备数据的结论，然后使用弱依赖规则进一步聚合所得到的高依赖性集合。最后，基于独立证据的任何结论可以使用独立的组合规则进行汇总。所得到的聚合决定了多数结论中最可能指出哪些故障，考虑到用于产生结论的设备数据的依赖性。

8. 发明申请

US20130091539A1 SYSTEM AND METHOD FOR INSIDER THREAT DETECTION 有权
标题翻译：内部威胁检测系统与方法
公开(公告)号：US20130091539A1
公开(公告)日：2013-04-11
申请号：US13270874
申请日：2011-10-11
申请人： Himanshu Khurana , Valerie Guralnik , Robert Shanley
发明人： Himanshu Khurana , Valerie Guralnik , Robert Shanley
IPC分类号： G06F21/00
CPC分类号： G06F21/552 , G06F21/554 , G06F21/577 , G06F2221/2141 , G06F2221/2145 , G07C9/00571 , H04L63/1425 , H04L63/1441
摘要： A system and method include obtaining data related to accessing cyber assets and accessing physical assets from a combined cyber access and physical access control system that protects cyber and physical assets of an organization from both authorized and unauthorized access with malicious intent. The system and method compare the data to known patterns of expected behavior, and identify patterns of suspicious behavior as a function of comparing the data to the patterns of expected behavior. The comparison is utilized to identify potentially malicious insider behavior toward the cyber and physical assets.
摘要翻译：一种系统和方法包括从组合的网络访问和物理访问控制系统获取有关访问网络资产和访问实体资产的数据，该系统可以防止组织的网络和实体资产受到授权和未经授权的访问恶意。系统和方法将数据与预期行为的已知模式进行比较，并将可疑行为的模式识别为将数据与预期行为模式进行比较的函数。该比较用于识别潜在的恶意内部人士对网络和实体资产的行为。

9. 发明授权

US08041539B2 Principal component analysis based fault classification 有权
标题翻译：基于主成分分析的故障分类
公开(公告)号：US08041539B2
公开(公告)日：2011-10-18
申请号：US12187975
申请日：2008-08-07
申请人： Valerie Guralnik , Wendy Foslien Graber
发明人： Valerie Guralnik , Wendy Foslien Graber
IPC分类号： G06F17/18
CPC分类号： G06K9/622 , G05B23/0254 , G06K9/6284
摘要： Principle Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events is identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.
摘要翻译：原理分量分析（PCA）用于对过程进行建模，采用聚类技术对基于PCA模型的传感器残差代表事件的偏移进行分组。 PCA模型对正常数据进行培训，然后运行包含正常数据的历史数据和包含事件的数据。事件的不良演员数据通过来自正常模型的Q（残差）和T2（异常方差）统计的偏移来识别，导致不良演员向量的时间序列。形成彼此类似的不良演员模式的群集，然后与事件相关联。

10. 发明申请

US20100228692A1 SYSTEM AND METHOD FOR MULTI-MODAL BIOMETRICS 审中-公开
标题翻译：多模式生物量的系统和方法
公开(公告)号：US20100228692A1
公开(公告)日：2010-09-09
申请号：US12715520
申请日：2010-03-02
申请人： Valerie Guralnik , Saad J. Bedros , Isaac Cohen
发明人： Valerie Guralnik , Saad J. Bedros , Isaac Cohen
IPC分类号： G06F15/18 , G06N5/02
CPC分类号： G06K9/6293 , G06K9/72
摘要： A system and method relate to multi-modal biometrics. A single modality score is generated for each of a plurality of biometric modalities. A classifier is selected from a database of multi-modal classifiers, and a multi-modal fusion is applied to the single modality scores using the classifier. The single modality scores are then aggregated. A context dependent model is generated, and a measure of the context in which the biometric samples were obtained is applied to the aggregated single modality scores. It is then determined whether there is a match between two or more biometric samples.
摘要翻译：一种系统和方法涉及多模态生物识别。为多个生物特征模式中的每一个生成单一模态分数。从多模态分类器的数据库中选择分类器，并且使用分类器将多模式融合应用于单个模态分数。然后聚合单一模态分数。生成上下文相关模型，并将生物特征样本获取的上下文的度量应用于聚合单模态分数。然后确定两个或更多个生物特征样本之间是否存在匹配。

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