Detecting Applications with Malicious Behavior in Android Device Based on GA and SVM

DOI

10.2991/ecae-17.2018.55How to use a DOI?

Keywords

malware detection; Android; n-gram; support vector machine; dalvik opcode; genetic algorithm

Abstract

In recent years, mobile technology and mobile-device have been rapidly developed. Since mobile devices collect and transmit large amounts of private information about users, malicious applications will pose a significant threat to the privacy and property security of the individual. Openness is a crucial factor why Android becomes the most popular mobile operate system, but it also results the Android system vulnerable to malware. In this paper, the n-gram opcode is employed to describe the applications, and then a static analysis method based on genetic algorithm and support vector machine is used to detect applications with malicious behaviors.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ning Liu
AU  - Min Yang
AU  - Shibin Zhang
PY  - 2017/12
DA  - 2017/12
TI  - Detecting Applications with Malicious Behavior in Android Device Based on GA and SVM
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
PB  - Atlantis Press
SP  - 257
EP  - 261
SN  - 2352-5401
UR  - https://doi.org/10.2991/ecae-17.2018.55
DO  - 10.2991/ecae-17.2018.55
ID  - Liu2017/12
ER  -