Abstract
The current Artificial Immune-Based malware detection systems research focus on traditional computers that uses information from OS or network, but the smartphone software behavior has its own structure and semantics. Current research cannot detect malware in smartphone exactly and efficiently. To address these problems, in this paper, An Artificial Immune-Based Smartphone Malware Detection Model named AIB-SPMDM is brought forwards and a prototype system is implemented, the experiment result show that the system can obtain higher detection rate and decrease the false positive rate.
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Zhao, M., Zhang, T., Wang, J., Yuan, Z. (2012). AIB-SPMDM: A Smartphone Malware Detection Model Based on Artificial Immunology. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_64
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DOI: https://doi.org/10.1007/978-3-642-34041-3_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34040-6
Online ISBN: 978-3-642-34041-3
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