Visual Detection for Android Malware using Deep Learning
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 18, 2020. | Revised Manuscript received on November 01, 2020. | Manuscript published on November 10, 2021. | PP: 152-156 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.A81321110120| DOI: 10.35940/ijitee.A8132.1110120
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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: The most serious threats to the current mobile internet are Android Malware. In this paper, we proposed a static analysis model that does not need to understand the source code of the android applications. The main idea is as most of the malware variants are created using automatic tools. Also, there are special fingerprint features for each malware family. According to decompiling the android APK, we mapped the Opcodes, sensitive API packages, and high-level risky API functions into three channels of an RGB image respectively. Then we used the deep learning technique convolutional neural network to identify Android application as benign or as malware. Finally, the proposed model succeeds to detect the entire 200 android applications (100 benign applications and 100 malware applications) with an accuracy of over 99% as shown in experimental results. 
Keywords: Android Malware, Malware Detection, Visual Analysis, Deep Learning, Image Processing.