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Published November 30, 2020 | Version v1
Journal article Open

Visual Detection for Android Malware using Deep Learning

  • 1. Systems and Computer Engineering Dept., Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
  • 1. Publisher

Description

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.

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Is cited by
Journal article: 2278-3075 (ISSN)

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ISSN
2278-3075
Retrieval Number
100.1/ijitee.A81321110120