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。