DroidMark – A Tool for Android Malware Detectionusing Taint Analysis and Bayesian Network

Abstract

With the increasing user-base of Android devices & advents of technologies such as Internet Banking, delicate user data is prone to be misused by malware and spyware applications. As the app-developer community increases, the quality reassurance could not be justified for every application and a possibility of data leakage arises. In this research, with the aim to ensure the application authenticity, Deep Learning methods and Taint Analysis are deployed on the applications. The detection system named DroidMark looks for possible sinks and sources of data leakage in the application by modelling Android’s lifecycles and callbacks, which is done by Reverse Engineering the APK, further monitoring the suspected processes and collecting data in different states of the application. DroidMark is thus designed to extract features from the applications which are fed to a trained Bayesian Network for classification of Malicious and Regular applications. The results indicate a high accuracy of 96.87% and an error rate of 3.13% in detection of Malware in Android devices.