›› 2012,Vol. 27 ›› Issue (3): 506-514.doi: 10.1007/s11390-012-1239-7

所属专题: Data Management and Data Mining

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Jian-Yun Liu1 (刘建芸), Yu-Hang Zhao1 (赵宇航), Member, CCF, Zhao-Xiang Zhang1 (张兆翔), Member, CCF, ACM, IEEE, Yun-Hong Wang1 (王蕴红), Member, CCF, ACM, IEEE, Xue-Mei Yuan2 (袁雪梅), Lei Hu2 (胡磊), Member, CCF, and Zhen-Jiang Dong2 (董振江)   

  • 收稿日期:2011-12-31 修回日期:2012-03-13 出版日期:2012-05-05 发布日期:2012-05-05

Spam Short Messages Detection via Mining Social Networks

Jian-Yun Liu1 (刘建芸), Yu-Hang Zhao1 (赵宇航), Member, CCF, Zhao-Xiang Zhang1 (张兆翔), Member, CCF, ACM, IEEE, Yun-Hong Wang1 (王蕴红), Member, CCF, ACM, IEEE, Xue-Mei Yuan2 (袁雪梅), Lei Hu2 (胡磊), Member, CCF, and Zhen-Jiang Dong2 (董振江)   

  1. 1. School of Computer Science and Engineering, Beihang University, Beijing 100191, China;
    2. ZTE Corporation, Nanjing 210012, China
  • Received:2011-12-31 Revised:2012-03-13 Online:2012-05-05 Published:2012-05-05
  • About author:Jian-Yun Liu received the B.E. degree from China University of Geo-sciences in 2009. He is now pursu-ing the M.S. degree in the Labora-tory of Intelligent Recognition and Image Processing at Beihang Univer-sity. His research interests include data mining, video analysis and pat-tern recognition.
  • Supported by:

    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.


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.

[1] Hidalgo J M G, Bringas G C, Sanz E P, Garcia F C. Contentbased SMS spam filtering. In Proc. the 2006 ACM Symposiumon Document Engineering, Amsterdam, Netherlands,Oct. 10-13, 2006, pp.107-114.

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[4] Yoo S, Yang Y, Lin F, Moon I. Mining social networks forpersonalized email prioritization. In Proc. the 15th ACMSIGKDD, Paris, France, June 28-July 1, 2009, pp.967-976.

[5] Gomes L H, Almeida R B, Bettencourt L M A, Almeida V,Almeida J M. Comparative graph theoretical characterizationof networks of spam and legitimate email. In Proc. Conf.Email and Anti-Spam, California, CA, USA, July 21-22, 2005.

[6] Newman M E J. Modularity and community structure innetworks. Proceedings of National Academy of Sciences ofU.S.A., May 2006, 103(23): 8577-8582.

[7] Goldberg A V. Finding a maximum density subgraph. Tech-nical Report, Uni. California, Berkeley, 1984. http://www.eecs.berkeley.edu/Pubs/TechRpts/1984/CSD-84-171.pdf.

[8] Grandoni F. A note on the complexity of minimum dominat-ing set. J. Discrete Algorithms, June 2006, 4(2): 209-214.

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[11] Almeida V, Bestavros A, Crovella M, de Oliveira A. Chara-cterizing reference locality in the WWW. In Proc. the 4th Int.Conf. Parallel and Distributed Information Systems, MiamiBeach, USA, Dec. 18-20, 1996, pp.92-103.
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