Abstract:
Android applications are widely used by
millions of users to perform many different activities.
Android-based smart phone users can get free
applications from Android Application Market. But,
these applications were not certified by legitimate
organizations and they may contain malware
applications that can steal private information from
users. The proposed system develops a permissionbased malware detection to protect the privacy of
android smart phone users. This system monitors
various permissions obtained from android
applications and analyses them by using a statistical
technique called singular value decomposition (SVD)
to estimate the correlations of permissions. The
training phase emphasizes on the malware samples
(approximately 300) downloaded from
https://www.kaggle.com/goorax/static-analysis-ofandroid-malware-of-2017. The proposed system
evaluates the risk level (High, Medium, and Low) of
Android applications based on the correlation
patterns of permissions. The system accuracy is 85%
for malware applications and goodware applications.
