The widespread adoption of Android devices and their capability to store access significant private and confidential information have resulted in these devices being targeted by malware developers. Existing Android malware analysis techniques can be broadly categorized into static and dynamic analysis. In this paper, we present two machine learning aided approaches for static analysis of Android malware. The first approach is based on permissions and the other is based on source code analysis utilizing a bag-of-words representation model. Our permission-based model is computationally inexpensive, and is implemented as the OWASP Seraphimdroid Android app that can be obtained from Google Play Store. Our evaluations of both approaches indicate an Fscore of 95.1% and F-measure of 89% for the source code-based classification and permission-based classification models, respectively.
Nikola et. al. estimated F-measure (does not take account of correctly classified benign apps) of 95.1% and 89% by classifying the apps based on source code and permission respectively REF .
Machine learning aided Android malware classification
Comput. Electr. Eng.
Comput. Electr. Eng.
Computer Science