ML for Android Malware Detection

Authors

  • Pravin P Kalyankar Assistant Professor, Department of Computer Science & Engineering, Brahmdevdada Mane Institute of Technology Solapur, University of PAH Solapur, Solapur, India

DOI:

https://doi.org/10.63671/ijsssr.v2i1.283

Keywords:

Android malware, SVM, MLP, KNN, PCA

Abstract

The past few years have witnessed the drastic increase of mobile apps providing various facilities for personal and business use. The proliferation of mobile apps is due to billions of users who enable developers to earn revenue through advertisements, in-app purchases, etc. Whenever users install a new app, they are under the risk of installing malware. Unlike desktop apps, mobile apps can have the privilege, after declared (e.g., in Manifest file of Android platform), to access sensitive information such as contact lists, SMS messages, GPS, etc. In this paper we proposed ML model for malware detection in the Android system. It predicts the malware from android data is to find the accuracy more reliable. In an Android Malware Detection using machine learning, ML algorithms can be employed to analyze and classify applications as either benign or malicious.

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Published

2024-05-01

Issue

Section

Articles

How to Cite

ML for Android Malware Detection. (2024). International Journal of Science and Social Science Research, 2(1), 148-151. https://doi.org/10.63671/ijsssr.v2i1.283

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