公开(公告)号：US09747440B2

公开(公告)日：2017-08-29

申请号：US13937462

申请日：2013-07-09

Applicant: QUALCOMM Incorporated

Inventor： Rajarshi Gupta ,  Mark Bapst ,  Mohammad H Reshadi ,  Samir Kumar

IPC: G06F21/55 ,  G06F9/445 ,  G06F21/31 ,  H04L29/06 ,  H04W12/12 ,  G06N5/04 ,  G06F11/30 ,  G06F11/34

CPC classification number: G06F21/55 ,  G06F8/61 ,  G06F8/65 ,  G06F11/3013 ,  G06F11/3409 ,  G06F11/3447 ,  G06F11/3476 ,  G06F21/316 ,  G06F21/552 ,  G06F2201/865 ,  G06N5/043 ,  H04L63/1425 ,  H04W12/12

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 probability of enabling a mobile device to better determine whether a mobile device behavior is malicious or benign. Based on this analysis, 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 better 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.

公开(公告)号：US20140053261A1

公开(公告)日：2014-02-20

申请号：US13937462

申请日：2013-07-09

Applicant: QUALCOMM Incorporated

Inventor： Rajarshi GUPTA ,  Mark Bapst ,  Mohammad H Reshadi ,  Samir Kumar

IPC: G06F21/55 ,  G06F9/445

CPC classification number: G06F21/55 ,  G06F8/61 ,  G06F8/65 ,  G06F11/3013 ,  G06F11/3409 ,  G06F11/3447 ,  G06F11/3476 ,  G06F21/316 ,  G06F21/552 ,  G06F2201/865 ,  G06N5/043 ,  H04L63/1425 ,  H04W12/12

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 probability of enabling a mobile device to better determine whether a mobile device behavior is malicious or benign. Based on this analysis, 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 better 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。 可以分析这样的载体以识别第一类分类器模型中的因素，其具有使移动设备能够更好地确定移动设备行为是恶意还是良性的最高概率。 基于该分析，可以生成第二类分类器模型，其识别显着更少的因子和数据点，以使得移动设备能够基于确定的因素更好地确定移动设备行为是恶意还是良性。 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。