A Machine Learning Approach to the Detection and Analysis of Android Malicious Apps

Abstract

Today, the use of mobile phone is growing in all the areas and unfortunately, it made the mobile phones a continuous target of cyber attackers. The main source of these kinds of attack is the malicious applications which a user will be downloading from trusted mediums such as Playstore, App store and all. Considering the millions of applications, the play store is having, it is impossible to identify which one is malicious and which one is not for a user. Even after the installation, the user will not be able to understand the activities the application will be performing in the mobile device. A lot of problems are arising nowadays because of this and a lot of confidential information is getting leaked from the mobile device. So, it is important to have a platform where it should be able to distinguish a malicious app from the set of benign app. This system is a mobile android application which will be working based on machine learning. The application will perform both static and dynamic analysis to identify the malicious activities of an application. The static analysis is mainly focused on the manifest.xml file of an Android application and the dynamic analysis will be based on the actions it will be triggering while running on a mobile device. The system is capable of combining both static and dynamic analysis results. The main aim of this project is to develop an efficient and effective android mobile application with a high success rate of distinguishing malicious from benign applications.