Abstract
Being the most used operating system makes Android vulnerable to various kinds of malware attacks. This is due to the fact that most applications require several permissions which are necessary for installation or smooth operability. Some Android applications are not certified by legitimate organisations and may contain malware which can steal private user information. This has increased the interest of applying machine learning algorithms for Android malware detection. In this research paper, we work with a dataset comprising of 215 permissions and API calls of 3799 Android applications. We evaluate machine learning classification algorithms like random forest, decision tree, Naive Bayes and support vector machine for detecting malicious Android applications based on permissions and API calls model. The results show that RF performs the best giving an accuracy of 98.15% using k-fold cross-validation for k = 5 and a mean accuracy of 97.79%.
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Singh, D., Karpa, S., Chawla, I. (2022). “Emerging Trends in Computational Intelligence to Solve Real-World Problems” Android Malware Detection Using Machine Learning. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_28
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DOI: https://doi.org/10.1007/978-981-16-3071-2_28
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