Performance Evaluation of Selected Machine Learning Techniques for Malware Detection in Android Devices

  • Shakirat Aderonke Salihu Department of Computer Science, University of Ilorin, Ilorin, Nigeria
  • Sodiq Quadri Department of Computer Science, University of Ilorin, Ilorin, Nigeria
  • Oluwakemi Christiana Abikoye Department of Computer Science, University of Ilorin, Ilorin, Nigeria
Keywords: Malware, Permissions, Classifier, Machine Learning, Security

Abstract

Android is the world’s largest and most popular Operating System for mobile devices. Its popularity keeps increasing as it has provided for its users, platform for creating applications and allowing them to be distributed. Due to its openness, it has become the favorite of most developers and smartphone users. As a result of rapid growth in sales of Android-based Smartphones in the mobile market, it becomes an ideal environment for malware writers to achieve their aim. The increase in mobile applications in android has also increased the development of malware that can exploit user’s information and make them vulnerable to hackers. Several studies have proposed machine learning algorithms for detecting Android malware. This paper did a comparative analysis of four different machine learning algorithms: SVM, K-means, Naïve Bayes and Decision Tree for detecting Android malware. The study was experimented using a total of 558 APK applications with 279malwares samples from MalGenome and 279 benign samples from the Google Play store. The accuracy of 94% was achieved with SVM, K-means, and Decision Tree while Naïve Bayes has 90% therefore, the study has demonstrated that the three classifiers with 94% accuracy are effective in detecting Android Malware.

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Published
2020-09-02
How to Cite
Salihu, S. A., Quadri, S., & Abikoye, O. C. (2020). Performance Evaluation of Selected Machine Learning Techniques for Malware Detection in Android Devices. Ilorin Journal of Computer Science and Information Technology, 3(1), 52 - 61. Retrieved from https://iljcsit.com.ng/index.php/ILJCSIT/article/view/PDF%20ILJCSIT_vol_3_no_1_pp%2052-61
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Articles