Analysis on Malware Detection with Multi Classifiers on M0Droid and DroidScreening Datasets

Abstract

The number of applications for smart mobiledevices is steadily growing with the continuousincrease in the utilization of these devices. theInstallation of malicious applications on smartdevices often arises the security vulnerabilities suchas seizure of personal information or the use of smartdevices in accordance with different purposes bycyber criminals. Therefore, the number of studies inorder to identify malware for mobile platforms hasincreased in recent years. In this study, permissionbasedmodel is used to detect the maliciousapplications on Android which is one of the mostwidely used mobile operating system. M0Droid andDroidScreening data sets have been analyzed usingthe Android application package files andpermission-based features extracted from these files.In our work, permission-based model which appliedpreviously across different data sets investigated toM0Droid and DroidScreening datasets and theexperimental results has been expanded. Whileobtaining results, feature set analyzed using differentclassification techniques. The results show thatpermission-based model is successful on M0Droidand DroidScreening data sets and Random Forestsoutperforms another method. When compared toM0Droid system model, it is obtained much bet terconclusions depend on success rate. Our approachprovides a method for automated static code analysisand malware detection with high accuracy andreduces smartphone malware analysis time