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Machine Learning-Based Android Malware Detection Using Manifest Permissions

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Title:Machine Learning-Based Android Malware Detection Using Manifest Permissions
Authors:Mcdonald, Jeffrey
Herron, Nathan
Glisson, William
Benton, Ryan
Keywords:Cybersecurity and Software Assurance
apk manifest
malware detection
show 1 morestatic analysis
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Date Issued:05 Jan 2021
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.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Cybersecurity and Software Assurance

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