Abstract
Short message service (SMS) is now becoming an indispensable way of social communication, and the problem of mobile spam is getting increasingly serious. We propose a novel approach for spam messages detection. Instead of conventional methods that focus on keywords or flow rate filtering, our system is based on mining under a more robust structure: the social network constructed with SMS. Several features, including static features, dynamic features and graph features, are proposed for describing activities of nodes in the network in various ways. Experimental results operated on real dataset prove the validity of our approach.
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This work is supported by the National Natural Science Foundation of China under Grant No. 60873158, the National Basic Research 973 Program of China under Grant No. 2010CB327902, the Fundamental Research Funds for the Central Universities of China, and the Opening Funding of the State Key Laboratory of Virtual Reality Technology and Systems of China.
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Liu, JY., Zhao, YH., Zhang, ZX. et al. Spam Short Messages Detection via Mining Social Networks. J. Comput. Sci. Technol. 27, 506–514 (2012). https://doi.org/10.1007/s11390-012-1239-7
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DOI: https://doi.org/10.1007/s11390-012-1239-7