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With the increasing user base of Android devices and advent of technologiessuch as Internet Banking, delicate user data is prone to be misused by malwareand spyware applications. As the app developer community increases, the qualityreassurance could not be justified for every application and a possibility ofdata leakage arises. In this research, with the aim to ensure the applicationauthenticity, Deep Learning methods and Taint Analysis are deployed on theapplications. The detection system named DroidMark looks for possible sinks andsources of data leakage in the application by modelling Android lifecycle andcallbacks, which is done by Reverse Engineering the APK, further monitoring thesuspected processes and collecting data in different states of the application.DroidMark is thus designed to extract features from the applications which arefed to a trained Bayesian Network for classification of Malicious and Regularapplications. The results indicate a high accuracy of 96.87% and an error rateof 3.13% in the detection of Malware in Android devices.

DroidMark: A Tool for Android Malware Detection using Taint Analysis and Bayesian Network