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1. 发明授权

US08533841B2 Deriving remediations from security compliance rules 有权
标题翻译：从安全合规规则中获得补救
公开(公告)号：US08533841B2
公开(公告)日：2013-09-10
申请号：US11695351
申请日：2007-04-02
申请人： Amit Raghunath Kulkarni , Arindam Chatterjee , Tristan Brown
发明人： Amit Raghunath Kulkarni , Arindam Chatterjee , Tristan Brown
IPC分类号： H04L9/32
CPC分类号： G06F21/552
摘要： Systems and methods that automatically generate remediation processes such as acts performed as part of a benchmark model, to improve and update compliance of a machine with security policies compliance. A remediation component can automatically determine processes that are required to change and increase compliance of a machine with a security policy, and hence improve security level thereof.
摘要翻译：自动生成修复过程的系统和方法，例如作为基准模型的一部分执行的操作，以改进和更新具有安全策略合规性的机器的合规性。修复组件可以自动确定改变并增加具有安全策略的机器的合规性所需的进程，从而提高其安全级别。

2. 发明申请

US20080244690A1 DERIVING REMEDIATIONS FROM SECURITY COMPLIANCE RULES 有权
标题翻译：从安全合规规则中获得补救
公开(公告)号：US20080244690A1
公开(公告)日：2008-10-02
申请号：US11695351
申请日：2007-04-02
申请人： Amit Raghunath Kulkarni , Arindam Chatterjee , Tristan Brown
发明人： Amit Raghunath Kulkarni , Arindam Chatterjee , Tristan Brown
IPC分类号： H04L9/00
CPC分类号： G06F21/552
摘要： Systems and methods that automatically generate remediation processes such as acts performed as part of a benchmark model, to improve and update compliance of a machine with security policies compliance. A remediation component can automatically determine processes that are required to change and increase compliance of a machine with a security policy, and hence improve security level thereof.
摘要翻译：自动生成修复过程的系统和方法，例如作为基准模型的一部分执行的操作，以改进和更新具有安全策略合规性的机器的合规性。修复组件可以自动确定改变并增加具有安全策略的机器的合规性所需的进程，从而提高其安全级别。

3. 发明授权

US09166797B2 Secured compartment for transactions 有权
标题翻译：安全隔间进行交易
公开(公告)号：US09166797B2
公开(公告)日：2015-10-20
申请号：US12257765
申请日：2008-10-24
申请人： Thekkthalackal Varugis Kurien , Cormac E. Herley , Alice Jane Bernheim Brush , Daniel C. Robbins , Arindam Chatterjee , Scott Field
发明人： Thekkthalackal Varugis Kurien , Cormac E. Herley , Alice Jane Bernheim Brush , Daniel C. Robbins , Arindam Chatterjee , Scott Field
IPC分类号： H04L9/32 , G06F13/24 , G07F7/08 , G07F7/10
CPC分类号： H04L9/3263 , G06F13/24 , G06F2221/2149 , G07F7/0826 , G07F7/1008 , H04L2209/56
摘要： Systems and methods that establish a secured compartment that manages sensitive user transactions/information on a user's machine. The secured compartment qualifies user interaction with the machine, and separates such qualified interaction from other user activity on the machine. A user is switched to such secured compartment upon occurrence of a predetermined event, such as in form of: an explicit request (e.g., a secure attention sequence); an implicit request (e.g., inference of user activities); and presence of a peripheral device that is bound to the secured compartment (e.g., a USB)—wherein such actions typically cannot be generated by an application running outside the secured compartment.
摘要翻译：建立安全隔间的系统和方法，用于管理用户机器上的敏感用户事务/信息。安全隔间限定用户与机器的交互，并将这种合格的交互与机器上的其他用户活动分开。在发生预定事件时，例如以明确的请求（例如，安全注意序列）的形式，用户切换到这样的安全隔间; 隐式请求（例如，推断用户活动）; 以及绑定到固定隔间（例如，USB）的外围设备的存在，其中这样的动作通常不能由在安全隔间外部运行的应用程序产生。

4. 发明申请

US20120216240A1 PROVIDING DATA SECURITY THROUGH DECLARATIVE MODELING OF QUERIES 审中-公开
标题翻译：通过查询的声明建模来提供数据安全
公开(公告)号：US20120216240A1
公开(公告)日：2012-08-23
申请号：US13029616
申请日：2011-02-17
申请人： Ramakanthachary Gottumukkala , Vijay Kurup , Srinivasan Parthasarathy , Edvardas Budrys , Tanmoy Dutta , Arindam Chatterjee
发明人： Ramakanthachary Gottumukkala , Vijay Kurup , Srinivasan Parthasarathy , Edvardas Budrys , Tanmoy Dutta , Arindam Chatterjee
IPC分类号： G06F17/00
CPC分类号： G06F21/6218
摘要： Data security is implemented through a query based policy constraining a primary table. Nested tables inherit the security policy by implementing the policy queries of the primary table. Operations on nested tables such as join actions execute the security policy queries once due to inheritance from the primary table therefore optimizing query modeling. A security policy may respond to a context or a role by executing queries responsive to the context.
摘要翻译：数据安全性通过限制主表的基于查询的策略实现。嵌套表通过实现主表的策略查询来继承安全策略。因为嵌套表的操作（如连接操作）由于从主表继承而执行安全策略查询，因此优化了查询建模。安全策略可以响应于上下文执行查询来响应上下文或角色。

5. 发明授权

US06907591B1 Method and apparatus for performing extraction using a neural network 有权
标题翻译：使用神经网络进行提取的方法和装置
公开(公告)号：US06907591B1
公开(公告)日：2005-06-14
申请号：US10062184
申请日：2002-01-31
申请人： Steven Teig , Arindam Chatterjee
发明人： Steven Teig , Arindam Chatterjee
IPC分类号： G06F17/50 , G06F9/45
CPC分类号： G06F17/5081 , G06F17/5036
摘要： A system for using machine-learning to create a model for performing integrated circuit layout extraction is disclosed. The system of the present invention has two main phases: model creation and model application. The model creation phase comprises creating one or more extraction models using machine-learning techniques. First, a complex extraction problem is decomposed into smaller simpler extraction problems. Then, each smaller extraction problem is then analyzed to identify a set of physical parameters that fully define the smaller extraction problem. Next, models are created using machine learning techniques for all of the smaller simpler extraction problems. The machine learning is performed by first creating training data sets composed of the identified parameters from typical examples of the smaller extraction problem and the answers to those example extraction problems as solved using a highly accurate physics-based field solver. Next, the system trains a set of neural networks using the training sets. In one embodiment, Bayesian inference is used to train the neural networks that are used to model the extraction. After the creation the neural network based models for each of the smaller simpler extraction problems, the neural network based models may be used for extraction.
摘要翻译：公开了一种使用机器学习创建用于执行集成电路布局提取的模型的系统。本发明的系统有两个主要阶段：模型创建和模型应用。模型创建阶段包括使用机器学习技术创建一个或多个提取模型。首先，复杂的提取问题被分解成更简单的提取问题。然后，分析每个较小的提取问题，以确定一组完全限定较小提取问题的物理参数。接下来，使用机器学习技术创建所有更简单的提取问题的模型。机器学习是通过首先创建由所识别的参数组成的训练数据集，从较小提取问题的典型示例和使用高度准确的基于物理的场求解器解决的那些示例提取问题的答案来进行。接下来，系统使用训练集来训练一组神经网络。在一个实施例中，贝叶斯推理用于训练用于建模提取的神经网络。在为每个较简单的提取问题创建基于神经网络的模型之后，基于神经网络的模型可用于提取。

6. 发明申请

US20100154026A1 AUTOMATED SOFTWARE RESTRICTION POLICY RULE GENERATION 有权
标题翻译：自动软件限制政策规则生成
公开(公告)号：US20100154026A1
公开(公告)日：2010-06-17
申请号：US12335549
申请日：2008-12-16
申请人： Arindam Chatterjee , Varugis Kurien , Bental Tagor , Sanjeev Dwivedi
发明人： Arindam Chatterjee , Varugis Kurien , Bental Tagor , Sanjeev Dwivedi
IPC分类号： H04L9/00 , G06F17/00
CPC分类号： G06F21/126 , G06F9/445 , G06F2221/2101
摘要： Software restriction policy rules can be automatically generated by parsing through a specified metadata source and generating the rules in accordance with indicated preferences. Metadata sources can include storage locations, such as folders, in which case rules for each executable file in the folder can be generated. Metadata sources can also include trusted publisher stores, installation logs, difference files, and other like data sources. Indicated preferences can select from among rules based on the publisher, for files that are signed, or rules based on hashes or path information for unsigned files. In generating rules to prevent the execution of specified files, if an optimized set of rules is desired, a check can be made to determine if an exception to an existing rule can be generated instead of a new rule. The automated parsing of the indicated metadata source can provide for both completeness and correctness.
摘要翻译：软件限制策略规则可以通过解析指定的元数据源并根据指定的偏好生成规则来自动生成。元数据源可以包括存储位置，例如文件夹，在这种情况下，可以生成文件夹中每个可执行文件的规则。元数据源还可以包括可信的发布商存储，安装日志，差异文件和其他类似的数据源。指示的首选项可以基于发布者的规则，对于已签名的文件，或基于散列的规则或未签名文件的路径信息进行选择。在生成规则以防止执行指定文件时，如果需要优化的一组规则，则可以进行检查以确定是否可以生成现有规则的异常而不是新规则。指定的元数据源的自动解析可以提供完整性和正确性。

7. 发明申请

US20090007264A1 SECURITY SYSTEM WITH COMPLIANCE CHECKING AND REMEDIATION 有权
标题翻译：具有合规检查和补救的安全系统
公开(公告)号：US20090007264A1
公开(公告)日：2009-01-01
申请号：US11768596
申请日：2007-06-26
申请人： Arindam Chatterjee , Anders Samuelsson , Nils Dussart , Charles G. Jeffries , Amit R. Kulkarni
发明人： Arindam Chatterjee , Anders Samuelsson , Nils Dussart , Charles G. Jeffries , Amit R. Kulkarni
IPC分类号： G06F11/00
CPC分类号： G06F21/577
摘要： A security system is provided for use with computer systems. In various embodiments, the security system can analyze the state of security of one or more computer systems to determine whether the computer systems comply with expressed security policies and to remediate the computer systems so that they conform with the expressed security policies. In various embodiments, the security system can receive compliance documents, determine whether one or more computer systems comply with portions of security policies specified in the compliance documents, and take actions specified in the compliance documents to cause the computer systems to comply with the specified security policies. The security system may provide a common, unified programming interface that applications or tools can employ to verify or enforce security policies.
摘要翻译：提供了一种用于计算机系统的安全系统。在各种实施例中，安全系统可以分析一个或多个计算机系统的安全状态，以确定计算机系统是否符合所表达的安全策略并修复计算机系统，使得它们符合所表达的安全策略。在各种实施例中，安全系统可以接收合规文件，确定一个或多个计算机系统是否符合合规文件中指定的安全策略的一部分，并采取合规文件中指定的措施，使计算机系统符合指定的安全性政策。安全系统可以提供一个通用的，统一的编程接口，应用程序或工具可以用来验证或执行安全策略。

8. 发明授权

US06880138B1 Method and apparatus for creating a critical input space spanning set of input points to train a machine learning model for extraction 有权
标题翻译：用于创建跨越一组输入点的关键输入空间以训练用于提取的机器学习模型的方法和装置
公开(公告)号：US06880138B1
公开(公告)日：2005-04-12
申请号：US10335097
申请日：2002-12-31
申请人： Steven Teig , Arindam Chatterjee
发明人： Steven Teig , Arindam Chatterjee
IPC分类号： G06F17/50
CPC分类号： G06F17/5036 , G06F2217/08
摘要： The present invention introduces novel methods of generating input vectors for machine learning system that will perform extraction. Experimental design is employed to select a set of training points that provide the best information. In one embodiment, a set of input vectors and output vectors are analyzed to determine a set of critical input parameters. Then, a spanning point generation program is used to generate a set of spanning points that cover the identified critical input space. The training point set then used to train a machine learning model.
摘要翻译：本发明引入了将执行提取的机器学习系统的生成输入向量的新颖方法。采用实验设计来选择一组提供最佳信息的训练点。在一个实施例中，分析一组输入向量和输出向量以确定一组关键输入参数。然后，使用跨点生成程序来生成覆盖所识别的关键输入空间的一组跨越点。训练点随后用于训练机器学习模型。

9. 发明授权

US06735748B1 Method and apparatus for performing extraction using a model trained with bayesian inference 有权
标题翻译：使用经贝叶斯推理训练的模型进行提取的方法和装置
公开(公告)号：US06735748B1
公开(公告)日：2004-05-11
申请号：US10062185
申请日：2002-01-31
申请人： Steven Teig , Arindam Chatterjee
发明人： Steven Teig , Arindam Chatterjee
IPC分类号： G06F945
CPC分类号： G06N7/005 , G06F17/5036
摘要： A machine-learning model may be created to perform integrated circuit layout extraction. Using such a machine-learning system has two main phases: model creation and model application. The model creation phase comprises creating one or more extraction models using machine-learning techniques. The machine learning is performed by first creating training data sets composed of the identified parameters from typical examples of the smaller extraction problem and the answers to those example extraction problems as solved using a highly accurate physics-based field solver. Next, the system performs machine learning using Bayesian inference in order to train the neural network models. The Bayesian inference may be implemented with normal Monte Carlo techniques, Hybrid Monte Carlo techniques, or other Bayesian learning techniques. After the creation of a set of models for each of the smaller simpler extraction problems, the machine-learning based models may be used for extraction.
摘要翻译：可以创建机器学习模型来执行集成电路布局提取。使用这样的机器学习系统有两个主要阶段：模型创建和模型应用。模型创建阶段包括使用机器学习技术创建一个或多个提取模型。机器学习是通过首先创建由所识别的参数组成的训练数据集，从较小提取问题的典型示例和使用高度准确的基于物理的场求解器解决的那些示例提取问题的答案来进行。接下来，系统使用贝叶斯推理进行机器学习，以训练神经网络模型。贝叶斯推理可以用正常的蒙特卡罗技术，混合蒙特卡罗技术或其他贝叶斯学习技术来实现。在为每个更简单的提取问题创建一组模型之后，可以使用基于机器学习的模型来提取。

10. 发明授权

US06687887B1 Method and apparatus for performing extraction using a model trained with Bayesian inference using a hybrid monte carlo method 有权
标题翻译：使用混合蒙特卡罗方法使用贝叶斯推理训练的模型进行提取的方法和装置
公开(公告)号：US06687887B1
公开(公告)日：2004-02-03
申请号：US10061437
申请日：2002-01-31
申请人： Steven Teig , Arindam Chatterjee
发明人： Steven Teig , Arindam Chatterjee
IPC分类号： G06F1750
CPC分类号： G06F17/5036 , G06F2217/08
摘要： A system for using machine learning based upon Bayesian inference using a hybrid monte carlo method to create a model for performing integrated circuit layout extraction is disclosed. The system of the present invention has two main phases: model creation and model application. The model creation phase comprises creating one or more extraction models using machine-learning techniques. First, a complex extraction problem is decomposed into smaller simpler extraction problems. Then, each smaller extraction problem is then analyzed to identify a set of physical parameters that fully define the smaller extraction problem. Next, complex mathematical models are created using machine learning techniques for all of the smaller simpler extraction problems. The machine learning is performed by first creating training data sets composed of the identified parameters from typical examples of the smaller extraction problem and the answers to those example extraction problems as solved using a highly accurate physics-based field solver. The system uses Bayesian inference implemented with a hybrid Monte Carlo method to train a set of neural networks for extraction problems. After the creation of a set of models for each of the smaller simpler extraction problems, the machine-learning based models may be used for extraction.
摘要翻译：公开了一种使用基于贝叶斯推理的机器学习的系统，其使用混合蒙特卡罗方法来创建用于执行集成电路布局提取的模型。本发明的系统有两个主要阶段：模型创建和模型应用。模型创建阶段包括使用机器学习技术创建一个或多个提取模型。首先，复杂的提取问题被分解成更简单的提取问题。然后，分析每个较小的提取问题，以确定一组完全限定较小提取问题的物理参数。接下来，使用机器学习技术创建复杂的数学模型，用于所有较小的较简单的提取问题。机器学习是通过首先创建由所识别的参数组成的训练数据集，从较小提取问题的典型示例和使用高度准确的基于物理的场求解器解决的那些示例提取问题的答案来进行。该系统使用采用混合蒙特卡罗方法实现的贝叶斯推理来训练一组提取问题的神经网络。在为每个更简单的提取问题创建一组模型之后，可以使用基于机器学习的模型来提取。

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