Abstract
Along with the rapid development of new science and technology, smartphone functionality has become more attractive. Smartphones not only bring convenience to the public but also the security risks at the same time through the installation of malicious applications. Among these, Android ransomware is gaining momentum and there is a need for effective defense as it is very important to ensure the security of smartphone user. There are various analysis techniques used to detect instances of Android ransomware. In this paper, we proposed the Android ransomware detection using dynamic analysis technique. Two dataset were used which is ransomware and benign dataset. The proposed approach used the system calls as features which obtained from dynamic analysis. The classification algorithms Random Forest, J48, and Naïve Bayes were used to classify the instances based on the proposed features. The experimental results showed that the Random Forest Algorithm achieved the highest detection accuracy of 98.31% with lowest false positive rate of 0.016.
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Acknowledgments
This research is sponsored by Universiti Tun Hussein Onn Malaysia (UTHM) via UTHM Registrar Office and Tier 1 Research Grant H237. The authors would like to thank Universiti Tun Hussein Onn Malaysia and Ministry of Higher Education Malaysia for the facilities and financially supporting this research.
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Abdullah, Z., Muhadi, F.W., Saudi, M.M., Hamid, I.R.A., Foozy, C.F.M. (2020). Android Ransomware Detection Based on Dynamic Obtained Features. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_12
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DOI: https://doi.org/10.1007/978-3-030-36056-6_12
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