出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Android being a widely used mobile platform has witnessed an increase in the number ofmalicious samples on its market place. The availability of multiple sources for downloadingapplications has also contributed to users falling prey to malicious applications. Classificationof an Android application as malicious or benign remains a challenge as malicious applicationsmaneuver to pose themselves as benign. This paper presents an approach which extractsvarious features from Android Application Package file (APK) using static analysis andsubsequently classifies using machine learning techniques. The contribution of this workincludes deriving, extracting and analyzing crucial features of Android applications that aid inefficient classification. The analysis is carried out using various machine learning algorithmswith both weighted and non-weighted approaches. It was observed that weighted approachdepicts higher detection rates using fewer features. Random Forest algorithm exhibited highdetection rate and shows the least false positive rate.
