WO2013173000A3 - On-device real-time behavior analyzer - Google Patents

On-device real-time behavior analyzer Download PDF

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Publication number
WO2013173000A3
WO2013173000A3 PCT/US2013/035943 US2013035943W WO2013173000A3 WO 2013173000 A3 WO2013173000 A3 WO 2013173000A3 US 2013035943 W US2013035943 W US 2013035943W WO 2013173000 A3 WO2013173000 A3 WO 2013173000A3
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WO
WIPO (PCT)
Prior art keywords
benign
decision tree
behavior
boosted decision
factors
Prior art date
Application number
PCT/US2013/035943
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French (fr)
Other versions
WO2013173000A2 (en
Inventor
Rajarshi Gupta
Xuetao WEI
Anil GATHALA
Vinay Sridhara
Original Assignee
Qualcomm Incorporated
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Qualcomm Incorporated filed Critical Qualcomm Incorporated
Publication of WO2013173000A2 publication Critical patent/WO2013173000A2/en
Publication of WO2013173000A3 publication Critical patent/WO2013173000A3/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/043—Distributed expert systems; Blackboards

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.
PCT/US2013/035943 2012-05-14 2013-04-10 On-device real-time behavior analyzer WO2013173000A2 (en)

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
US201261646590P 2012-05-14 2012-05-14
US61/646,590 2012-05-14
US201261683274P 2012-08-15 2012-08-15
US61/683,274 2012-08-15
US201361748217P 2013-01-02 2013-01-02
US61/748,217 2013-01-02
US13/773,247 US9324034B2 (en) 2012-05-14 2013-02-21 On-device real-time behavior analyzer
US13/773,247 2013-02-21

Publications (2)

Publication Number Publication Date
WO2013173000A2 WO2013173000A2 (en) 2013-11-21
WO2013173000A3 true WO2013173000A3 (en) 2014-01-09

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PCT/US2013/035943 WO2013173000A2 (en) 2012-05-14 2013-04-10 On-device real-time behavior analyzer

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US (1) US9324034B2 (en)
WO (1) WO2013173000A2 (en)

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