Welcome to the InfoSci Platform
A Comparative Study of Machine Learning Techniques for Android Malware Detection

A Comparative Study of Machine Learning Techniques for Android Malware Detection

Mohamed Guendouz, Abdelmalek Amine
Copyright: © 2022 |Volume: 10 |Issue: 1 |Pages: 13
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781683182832|DOI: 10.4018/IJSI.309719
Cite Article Cite Article

MLA

Guendouz, Mohamed, and Abdelmalek Amine. "A Comparative Study of Machine Learning Techniques for Android Malware Detection." IJSI vol.10, no.1 2022: pp.1-13. http://doi.org/10.4018/IJSI.309719

APA

Guendouz, M. & Amine, A. (2022). A Comparative Study of Machine Learning Techniques for Android Malware Detection. International Journal of Software Innovation (IJSI), 10(1), 1-13. http://doi.org/10.4018/IJSI.309719

Chicago

Guendouz, Mohamed, and Abdelmalek Amine. "A Comparative Study of Machine Learning Techniques for Android Malware Detection," International Journal of Software Innovation (IJSI) 10, no.1: 1-13. http://doi.org/10.4018/IJSI.309719

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The rapid growth and wide availability of Android applications in recent years has resulted in a spike in the number of sophisticated harmful applications targeting Android users. Because of the popularity and amount of open-sourced supported features of Android OS, cyber attackers prefer to target Android-based devices over other smartphones. Malicious programs endanger user privacy and device integrity. To address this issue, the authors investigated machine learning algorithms for detecting malware in Android in this study. They employed a static analysis approach, collecting permissions from each application's APK and then generating feature vectors based on the extracted permissions. Finally, they trained several machine learning algorithms to create classification models that can distinguish between benign and malicious applications. According to experimental findings, random forest and multi-layer perceptron approaches, which have accuracy levels of 95.4% and 95.1%, respectively, have the best classification performance.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.