Advanced SearchSearch Tips
Coward Analysis based Spam SMS Detection Scheme
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Coward Analysis based Spam SMS Detection Scheme
Oh, Hayoung;
  PDF(new window)
Analyzing characteristics of spam text messages had limitations since spam datasets are typically difficult to obtain publicly and previous studies focused on spam email. Although existing studies, such as through the use of spam e-mail characterization and utilization of data mining techniques, there are limitations that influence is limited to high spam detection techniques using a single word character. In this paper, we reveal the characteristics of the spam SMS based on experiment and analysis from different perspectives and propose coward analysis based spam SMS detection scheme with a publicly disclosed spam SMS from the University of Singapore. With the extensive performance evaluations, we show false positive and false negative of the proposed method is less than 2%.
Properties of spam SMS;coword analysis;false positive;false negative;
 Cited by
T. M. Mahmoud and A. M. Mahfouz, "SMS Spam Filtering Technique Based on Artificial Immune System," International Journal of Computer Science Issues (IJCSI), vol. 9, pp. 589-597, Mar. 2012.

T. A. Almeida, J. M. G. Hidalgo, and A. Yamakami, "Contributions to the study of SMS spam filtering: new collection and results," in Proceedings of the 11th ACM symposium on Document engineering, pp. 259-262, Sep. 2011.

J. M. Gomez Hidalgo, G. Cajigas Bringas, E. Puertas Sanz, and F. Carrero Garcia, "Content Based SMS Spam Filtering," in Proceedings of the 2006 ACM Symposium on Document Engineering, Amsterdam, The Netherlands, pp. 107-114, Oct. 2006.

Gordon V. Cormack, Jose Maria Gomez Hidalgo and Enrique Puertas Sanz, "Feature Engineering for Mobile (SMS) Spam Filtering", ACM SIGIR'07, pp. 871-872, Jul. 2007.

Huang Jie, Huang Bei and Pu Wenjing, "A Bayesian Approach for Text Filter on 3G Network", In Proceedings of the 6th International Conference on Wireless Communications Networking and Mobile Computing, pp. 1-5, Sep. 2010.

Wuying Liu and Ting Wang, "Index-based Online Text Classification for SMS Spam Filtering", Journal of Computers, vol. 5, no. 6, pp. 844-851, Jan. 2010.

Xia Hu and Fu Yan, "Sampling of Mass SMS Filtering Algorithm Based on Frequent Time-domain Area", In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 548-551, Jan. 2010.

Zi Chu, Steven Gianvecchio, Haining Wang and Sushil Jajodia, "Who is tweeting on twitter: human, bot, or cyborg?", In Gates et al., pp. 21-30, Dec. 2010.

Sarah Jane Delany, Mark Buckley and Derek Greene, "SMS Spam Filtering: Methods and Data", Expert Systems with Applications, vol. 39, no. 10, pp. 9899-9908, Dec. 2012. crossref(new window)

Almeida, T.A., Gomez Hidalgo, J.M., Silva, T.P. "Towards SMS Spam Filtering: Results under a New Dataset", International Journal of Information Security Science (IJISS), vol. 2, no. 1, pp. 1-18, Jan. 2013.