Deep-Droid: Deep Learning for Android Malware Detection
Ahmed Hashem El Fiky

Ahmed Hashem El Fiky*, Systems and Computer Engineering Dept. Faculty of Engineering, Al-Azhar University, Cairo, Egypt. 

Manuscript received on September 12, 2020. | Revised Manuscript received on September 23, 2020. | Manuscript published on October 10, 2020. | PP: 122-125 | Volume-9 Issue-12, October 2020 | Retrieval Number: 100.1/ijitee.L78891091220 | DOI: 10.35940/ijitee.L7889.1091220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. Machine learning approaches have been showing promising results in classifying malware where most of the method are shallow learners like Random Forest (RF) in recent years. In this paper, we propose Deep-Droid as a deep learning framework, for detection Android malware. Hence, our Deep-Droid model is a deep learner that outperforms exiting cutting-edge machine learning approaches. All experiments performed on two datasets (Drebin-215 & Malgenome-215) to assess our Deep-Droid model. The results of experiments show the effectiveness and robustness of Deep-Droid. Our Deep-Droid model achieved accuracy over 98.5%. 
Keywords: Android Malware, Malware Detection, Static Analysis, Deep-Droid, Deep-Learning.