Abstract
Important personal user information has become scattered in devices as mobile devices are now supporting various services and contents. Accordingly, attackers are expanding the scope of their attack not only in the existing PC and Internet environment but also to mobile devices. In this paper, we monitor the resource information of mobile devices to detect Android malware. Using the monitored information, we propose a method of detecting malware by applying linear SVM (support vector machine) that shows high classification performance in machine learning classifiers in order to automatically detect malware. The validity of the proposed methodology is verified through experiment results.
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Acknowledgments
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the MSIP (Ministry of Science, ICT and Future Planning) (2013R1A1A3011698).
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Ham, HS., Kim, HH., Kim, MS., Choi, MJ. (2014). Linear SVM-Based Android Malware Detection. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_68
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DOI: https://doi.org/10.1007/978-94-017-8798-7_68
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