Permission Based Anomalous Application Detection on Android Smart Phone

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.