公开(公告)号：US09495537B2

公开(公告)日：2016-11-15

申请号：US13923547

申请日：2013-06-21

Applicant: QUALCOMM Incorporated

Inventor： Rajarshi Gupta ,  Vinay Sridhara ,  Anil Gathala ,  Xuetao Wei

IPC: G06F21/00 ,  G06F21/50 ,  G06F21/31 ,  G06F21/55

CPC classification number: G06F21/50 ,  G06F21/316 ,  G06F21/552

Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources.

Abstract translation: 用于智能地，动态地和/或自适应地检测待观察的计算设备行为，要观察的行为的数量以及细节或粒度的级别来检测可疑或降级性能的移动设备行为的方法，设备和系统 在那里要观察移动设备的行为。 各个方面有效地识别可疑或降低性能的移动设备行为，而不需要过多的处理，存储器或能量资源。

公开(公告)号：US09324034B2

公开(公告)日：2016-04-26

申请号：US13773247

申请日：2013-02-21

Applicant: QUALCOMM Incorporated

Inventor： Rajarshi Gupta ,  Xuetao Wei ,  Anil Gathala ,  Vinay Sridhara

IPC: G06N99/00 ,  G06N5/04

CPC classification number: G06N99/005 ,  G06N5/043

Abstract: Methods, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign.

Abstract translation: 用于在通信系统中生成数据模型的方法，系统和设备可以包括应用机器学习技术来生成使用加强的决策树来描述行为矢量语料库的分类器模型的第一族。 可以使用这样的行为矢量来计算升压决策树的一个或多个节点的权重值。 可以识别分类器模型的因素，其可能基于所计算的权重值来确定移动设备行为是良性还是不良性。 用于升压的决策树节点的计算权重值可以包括计算生成的升压决策树节点的独占应答比率。 识别的因素可以应用于行为矢量语料库以产生第二类分类器模型，其识别与使移动设备能够确定行为是良性还是不良性相关的较少因素和数据点。

公开(公告)号：US20140053260A1

公开(公告)日：2014-02-20

申请号：US13923547

申请日：2013-06-21

Applicant: QUALCOMM Incorporated

Inventor： Rajarshi Gupta ,  Vinay Sridhara ,  Anil Gathala ,  Xuetao Wei

IPC: G06F21/50

CPC classification number: G06F21/50 ,  G06F21/316 ,  G06F21/552

Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources.

公开(公告)号：US20130304677A1

公开(公告)日：2013-11-14

申请号：US13776414

申请日：2013-02-25

Applicant: QUALCOMM INCORPORATED

Inventor： Rajarshi Gupta ,  Xuetao Wei ,  Anil Gathala ,  Vinay Srishara

IPC: G06N99/00

CPC classification number: G06N20/00 ,  G06F21/552 ,  G06F21/566 ,  G06N5/043

Abstract: Methods, systems and devices for generating data models in a client-cloud communication system may include applying machine learning techniques to generate a first family of classifier models that describe a cloud corpus of behavior vectors. Such vectors may be analyzed to identify factors in the first family of classifier models that have the highest probably of enabling a mobile device to conclusively determine whether a mobile device behavior is malicious or benign. Based on this analysis, a a second family of classifier models may be generated that identify significantly fewer factors and data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is malicious or benign based on the determined factors. A mobile device classifier module based on the second family of classifier models may be generated and made available for download by mobile devices, including devices contributing behavior vectors.

Abstract translation: 用于在客户云通信系统中生成数据模型的方法，系统和设备可以包括应用机器学习技术来生成描述行为矢量的云语料库的分类器模型的第一族。 可以分析这样的矢量以识别分类器模型的第一族中的因素，其中最可能使移动设备能够最终确定移动设备行为是恶意还是良性。 基于该分析，可以生成第二系列分类器模型，其识别显着更少的因子和数据点，使其与使得移动设备能够根据确定的因素最终确定移动设备行为是恶意还是良性有关。 可以生成基于第二类分类器模型的移动设备分类器模块，并使其可用于由移动设备（包括贡献行为矢量的设备）进行下载。

公开(公告)号：US20130304676A1

公开(公告)日：2013-11-14

申请号：US13773247

申请日：2013-02-21

Applicant: QUALCOMM INCORPORATED

Inventor： Rajarshi Gupta ,  Xuetao Wei ,  Anil Gathala ,  Vinay Sridhara

IPC: G06N99/00

CPC classification number: G06N99/005 ,  G06N5/043

Abstract: Methods, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign.

Abstract translation: 用于在通信系统中生成数据模型的方法，系统和设备可以包括应用机器学习技术来生成使用加强的决策树来描述行为矢量语料库的分类器模型的第一族。 可以使用这样的行为矢量来计算升压决策树的一个或多个节点的权重值。 可以识别分类器模型的因素，其可能基于所计算的权重值来确定移动设备行为是良性还是不良性。 用于升压的决策树节点的计算权重值可以包括计算生成的升压决策树节点的独占应答比率。 识别的因素可以应用于行为矢量语料库以产生第二类分类器模型，其识别与使移动设备能够确定行为是良性还是不良性相关的较少因素和数据点。