On-Device Mobile Phone Security Exploits Machine Learning

被引：10

作者：

Islam, Nayeem ^{[1
]}

Das, Saumitra ^{[1
]}

Chen, Yin ^{[1
]}

机构：

[1] Qualcomm, Santa Clara, CA 95051 USA

来源：

IEEE PERVASIVE COMPUTING | 2017年 / 16卷 / 02期

关键词：

hackers; malware; mobile; networking; pervasive computing; security;

D O I：

10.1109/MPRV.2017.26

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The authors present a novel approach to protecting mobile devices from malware that might leak private information or exploit vulnerabilities. The approach, which can also keep devices from connecting to malicious access points, uses learning techniques to statically analyze apps, analyze the behavior of apps at runtime, and monitor the way devices associate with Wi-Fi access points. © 2017 IEEE.

引用

页码：92 / 96

页数：5

共 11 条

[1] [Anonymous], P 20 ANN NETW DISTR
[2] [Anonymous], 2012, Adventures in bouncerland
[3] [Anonymous], 2014, P NETW DISTR SYST SE
[4] [Anonymous], 2012, Dissecting the Android Bouncer
[5] [Anonymous], 2011, PROC INT C MOBILE SY
[6] Felt Adrienne Porter, 2011, Proceedings of the 1st acm workshop on security and privacy in smartphones and mobile devices, P3
[7] Idika N, 2007, SURVEY MALWARE DETEC
[8] Kliarsky A., 2011, CISC VIS NETW IND GL
[9] Regenscheid A., 2012, ROOTS TRUST MOBILE D
[10] Unuchek R., 2016, MOBILE MALWARE EVOLU

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