An Android Malware Detection Method Based on Feature Codes
Yiran Li, Zhengping Jin
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.519How to use a DOI?
- android security, malware, feature codes, system call, machine learning.
- The Linux-based android operation system is now exposed to high risks of security since the malware of smart phone explodes. For the purpose of effectively detecting the malware on the android platform, an android malware detection method based on feature codes is described in this paper. By using the function call and system call, analyzed and extracted from the malware sample library, as the feature vectors which will be subject to training and classification upon machine learning and data mining algorithm, a feature library and a detection model is established. An android malware detection system, ANDect, is developed upon this method and used for detecting 350 malicious applications and 750 non-malicious applications. As the results, ANDect is proven that it can effectively find out the undiscovered malicious Applications of android by utilizing the feature vectors of codes from the android applications, with high accuracy and low false positive rate.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yiran Li AU - Zhengping Jin PY - 2015/12 DA - 2015/12 TI - An Android Malware Detection Method Based on Feature Codes BT - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015 PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/icmmcce-15.2015.519 DO - https://doi.org/10.2991/icmmcce-15.2015.519 ID - Li2015/12 ER -