Detecting Applications with Malicious Behavior in Android Device Based on GA and SVM
- https://doi.org/10.2991/ecae-17.2018.55How to use a DOI?
- malware detection; Android; n-gram; support vector machine; dalvik opcode; genetic algorithm
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.
- © 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 -