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An Efficient Approach of Deep Learning for Android Malware Detection

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Ahmad, M.S., Javeed, D., Shoaib, M., Younas, N. and Zaman, A., 2021. An Efficient Approach of Deep Learning for Android Malware Detection. United International Journal for Research & Technology (UIJRT), 2(11), pp.15-20.


Android plays a very important role in the development of mobile technology as it is one of the famous operating systems in smartphones. Its popularity makes it a target of different cyber-attacks that result in money loss and data loss. There is a severe need to protect the android operating system from such attacks. This paper implements a detection system using efficient DL algorithms (i.e., LSTM and BLSTM) by employing the latest publically available “CICAndMal2017” dataset in order to protect the android systems against numerous attacks. Further, it uses standard evaluation metrics for the measurement of the system’s performance. Finally, this paper aims to compare the results with the current state-of-the-art detection techniques to show the efficiency of the proposed model.

Keywords: Android, Threat Detection, Deep Learning, IDS.


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