Machine Learning-Based Android Malware Detection Using Manifest Permissions

Date

2021-01-05

Authors

Herron, Nathan
Glisson, William Bradley
McDonald, J. Todd
Benton, Ryan K.

Journal Title

Journal ISSN

Volume Title

Publisher

Proceedings of the 54th Hawaii International Conference on System Sciences

Abstract

The Android operating system is currently the most prevalent mobile device operating system holding roughly 54 percent of the total global market share. Due to Android’s substantial presence, it has gained the attention of those with malicious intent, namely, malware authors. As such, there exists a need for validating and improving current malware detection techniques. Automated detection methods such as anti-virus programs are critical in protecting the wide variety of Android-powered mobile devices on the market. This research investigates effectiveness of four different machine learning algorithms in conjunction with features selected from Android manifest file permissions to classify applications as malicious or benign. Case study results, on a test set consisting of 5,243 samples, produce accuracy, recall, and precision rates above 80%. Of the considered algorithms (Random Forest, Support Vector Machine, Gaussian Naïve Bayes, and K-Means), Random Forest performed the best with 82.5% precision and 81.5% accuracy.

Description

Paper co-authored by William Bradley Glisson that was is published in the Proceedings of the 54th Hawaii International Conference on System Science in 2021

Keywords

Cybersecurity and Software Assurance, android, anti-virus, apk manifest, malware detection, static analysis

Citation

McDonald, J. T., Herron, N., Glisson, W. B., Benton, R. K.,(2021). Machine Learning-Based Android Malware Detection Using Manifest Permission. Proceedings of the 54th Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2021.839