Abstract
In the world today, smartphones are evolving every day and with this evolution, security becomes a big issue. Security is an important aspect of the human existence and in a world, with inadequate security, it becomes an issue for the safety of the smartphone users. One of the biggest security threats to smartphones is the issue of malware. The study carried out a survey on malware detection techniques towards identifying gaps, and to provide the basis for improving and effective measure for unknown android malware. The results showed that machine learning is a more promising approach with higher detection accuracy. Upcoming researchers should look into deep learning approach with the use of a large dataset in order to achieve a better accuracy.
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We acknowledge the support and sponsorship provided by Covenant University through the Centre for Research, Innovation, and Discovery (CUCRID).
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Odusami, M., Abayomi-Alli, O., Misra, S., Shobayo, O., Damasevicius, R., Maskeliunas, R. (2018). Android Malware Detection: A Survey. In: Florez, H., Diaz, C., Chavarriaga, J. (eds) Applied Informatics. ICAI 2018. Communications in Computer and Information Science, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-01535-0_19
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DOI: https://doi.org/10.1007/978-3-030-01535-0_19
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