Android Malware Detection using Deep Learning

Abstract

Android has become the most standard smartphone operating system. The rapidly growing acceptance of android has resulted in significant increase in the number of malwares when compared with earlier years. There exists plenty of antimalware programs which are designed to efficiently protect the user's sensitive data in mobile systems from such attacks. Here, I have examined the different android malwares and their methods based on deep learning that are used for attacking the devices and antivirus programs that act against malwares to care for Android systems. Then, we have discuss on different deep learning based android malware detection techniques such as, Maldozer, DroidDetector, DroidDeepLearner, DeepFlow, Droid Delver and Droid Deep. We aim to implement a model based on deep learning that can automatically identify whether an android application is malware infected or not without installation.