  With the increasing user base of Android devices and advent 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 lifecycle 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 the detection of Malware in Android devices.
