Abstract
Android malware is a significant threat to Android-based devices. Various single-app analysis tools are developed to detect these threats. In this regard, ML-based tools are more effective in detecting single malicious applications due to their robustness and capability to detect zero-day malware. In this work, we propose ensemble classifiers composed of support vector machines (SVMs) to detect Android malware. We discuss the proposed classifiers’ effectiveness and do a comparative analysis with existing single SVM-based classifiers, a mixture of SVM and other classifiers, and multi-stage classifiers based on SVM. We also argue that the proposed classifiers can detect app-collusion, a special kind of threat.
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Faiz, M.F.I. (2021). SVM-Based Ensemble Classifiers to Detect Android Malware. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-030-75078-7_35
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DOI: https://doi.org/10.1007/978-3-030-75078-7_35
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