The analysis of feature selection methods and classification algorithms in permission based Android malware detection

摘要: Android mobile devices have reached a widespread use since the past decade, thus leading to an increase in number and variety of applications on market. However, from perspective information security, user control sensitive has been shadowed by fast development rich applications. In recent state art, users are subject responding numerous requests for permission about using their private data be able run application. The awareness protection its relationship is crucial protecting against malicious software. Nevertheless, slow adaptation novel technologies suggests need developing automatic tools detecting present study, we analyze two major aspects permission-based malware detection applications: Feature selection methods classification algorithms. Within framework assumptions specified analysis used analysis, our findings reveal higher performance Random Forest J48 decision tree algorithms most selected feature methods.