Detecting Malicious Applications on Android is based on Static Analysis using Machine Learning Algorithm

Abstract

Attacks on users through mobile devices in general, and mobile devices with Android operating system in particular, have been causing many serious consequences. Research [1] lists the vulnerabilities found in the Android operating system, making it the preferred target of cyberattackers. Report [2] statistics the number of cyberattacks via mobile devices and mobile devices using Android operating system. The report points out the insecurity of information from applications downloaded by users from Android apps stores. Therefore, to prevent the attack and distribution of malware through Android apps, it is necessary to research the method of detecting malicious code from the time users download applications to their devices. Recent approaches often rely on static analysis and dynamic analysis to look for unusual behavior in applications. In this paper, we will propose the use of static analysis techniques to build a behavior of malicious code in the application and machine learning algorithms to detect malicious behavior.