Download PDFOpen PDF in browserAndroid Malware Detection using Deep LearningEasyChair Preprint 769413 pages•Date: April 2, 2022AbstractA large part of Android's popularity is due to the ease with which it can be operated and the wide range of capabilities it offers its users, both of which have made it a prime target for cyber thieves. Recent malware may get through the cracks of Android's traditional anti-malware solutions, such as those that rely on signatures or monitor power use. This new method uses NEURAL Networks and the KMEANS algorithm to develop a predictive analytics model to identify virus in Android applications. By extracting the permissions list from the Android APK file, we can use these two techniques to detect malware in Android apps. We use the Android Permission Features dataset to develop and evaluate our strategy. Our deep learning method exceeds other methods with a 99 percent accuracy rate, according to the results of our tests. Static analysis, API-calls, and permissions are all examples of Android malware. Keyphrases: Android, Malware, deep learning
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