Machine Learning-Based Android Malware Detection Using Manifest Permissions

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dc.contributor.authorHerron, Nathan
dc.contributor.authorGlisson, William Bradley
dc.contributor.authorMcDonald, J. Todd
dc.contributor.authorBenton, Ryan K.
dc.date.accessioned2021-09-15T15:05:58Z
dc.date.available2021-09-15T15:05:58Z
dc.date.issued2021-01-05
dc.descriptionPaper co-authored by William Bradley Glisson that was is published in the Proceedings of the 54th Hawaii International Conference on System Science in 2021en_US
dc.description.abstractThe 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.en_US
dc.identifier.citationMcDonald, 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.2021.839en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3195
dc.publisherProceedings of the 54th Hawaii International Conference on System Sciencesen_US
dc.subjectCybersecurity and Software Assuranceen_US
dc.subjectandroiden_US
dc.subjectanti-virusen_US
dc.subjectapk manifesten_US
dc.subjectmalware detectionen_US
dc.subjectstatic analysisen_US
dc.titleMachine Learning-Based Android Malware Detection Using Manifest Permissionsen_US
dc.typeArticleen_US

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