A Neural Network Based Hybrid Approach for Analysing and Detecting Malware Threat in Android Applications


  • Hetal Suresh
  • Joseph Raymond V






Android, Malicious Application, Machine Learning, Hybrid approach, RNN


Mobile phones has become very integral part in our day to day life. In the digitalized world most of our day to day activities rely on mobile phone like banking activities, wallet payments, credentials, social accounts etc. Our system works in such a way that if there is an advantage to a technology there also exists a disadvantage. Every users have all their private and sensitive data in their mobile phones and download random applications from different platforms like play store, App store etc. There is a huge possibility that the applications downloaded are malicious applications. The existing system provides a solution for detection of such applications with the help of antivirus which has pre-built signatures that can be used to obtain an already existing malware which can be modified and manipulated by the hacker if they tend to do so. In this project, our purpose is to identify the malicious applications using Machine learning. By combining both static analysis and dynamic analysis we can use a Hybrid approach for analysing and detecting malware threats in android applications using Recurrent Neural Network (RNN). The main aim of this project will be to ensure that the application installed is benign, if it is not, it should block such applications and notify the user.



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