Abstract:
Android has become a predominant mobile operating system lately. Google with the help of
Open Handset Alliance setting out to create open standards for smart phones have prompted a
gigantic development in the digital world. Given the growth and development of smartphone
devices and their related application stores, Malware detection is a developing issue. Volume
of new applications is excessively enormous to physically analyze every application for
malicious activity. Keeping this in view this research presents a method to detect android
malware and further classify it to four malware categories and thirty nine malware families.
The classification model has been built around reduction of redundant features and employing
three machine learning algorithms (Random Forest, KNN and SVM algorithms) in binary
classification and Random forest algorithm for category and family classification. The
proposed methodology performs reasonably well for most of the classes achieving around an
accuracy of 95% on binary classification. Proposed method provides the accuracy of 84% on
malware category classification and accuracy of 66% for Malware family classification.
