Analysis of Permissions Correlation for Android Apps Using Statistical SVD Approach

Abstract

Nowadays, almost all the users use Android applications in their smart phones for various reasons Since Android is free operating system, android-apps can be easily downloaded via biggest open app stores and third-party mobile app markets. But these applications were not guaranteed whether these are malware apps or not by legitimate organizations. As mobile phones are glued with most of the people, malware applications threaten all of them for their private information. So, the work of analysis for the apps is very important. The proposed system analyzes the correlation patterns of app’s permissions that must be used in all android apps by developers by using a statistical technique called singular value decomposition (SVD). The analysis phase uses the numbers of malware samples 50 to 300 from https://www.kaggle.com/goorax/static-analysis-of-android-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 both malware and goodware applications. Nowadays, almost all the users use Android applications in their smart phones for various reasons Since Android is free operating system, android-apps can be easily downloaded via biggest open app stores and third-party mobile app markets. But these applications were not guaranteed whether these are malware apps or not by legitimate organizations. As mobile phones are glued with most of the people, malware applications threaten all of them for their private information. So, the work of analysis for the apps is very important. The proposed system analyzes the correlation patterns of app’s permissions that must be used in all android apps by developers by using a statistical technique called singular value decomposition (SVD). The analysis phase uses the numbers of malware samples 50 to 300 from https://www.kaggle.com/goorax/static-analysis-of-android-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 both malware and goodware applications.