Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)

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

Authors
Ning Liu, Min Yang, Shibin Zhang
Corresponding Author
Ning Liu
Available Online December 2017.
DOI
https://doi.org/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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Volume Title
Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
Series
Advances in Engineering Research
Publication Date
December 2017
ISBN
978-94-6252-458-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/ecae-17.2018.55How to use a DOI?
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/).

Cite this article

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  - https://doi.org/10.2991/ecae-17.2018.55
ID  - Liu2017/12
ER  -