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Android Malware Detection Using Deep Learning
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DOI：10.1016/j.procs.2021.03.106.png)
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The Android operating system ranks first in the market share due to the system's smooth handling and many other features that it provides to Android users, which has attracted cyber criminals. Traditional Android malware detection methods, such as signature-based methods or methods monitoring battery consumption, may fail to detect recent malware. Therefore, we present a novel method for detecting malware in Android applications using Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). We extract two static features, namely, Application Programming Interface (API) calls and Permissions from Android applications. We train and test our approach using CICAndMa12017 dataset. The experimental results show that our deep learning method outperforms several methods with accuracy of 98.2%. (C) 2021 The Authors. Published by Elsevier B.V.
Keyword:
Android Malware
Static analysis
API-calls
Permissions
Gated Recurrent Unit
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A Review of Android Malware Detection Approaches Based on Machine Learning基于机器学习的Android恶意软件检测方法综述.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
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