Advertisement

A New SMS Spam Detection Method Using Both Content-Based and Non Content-Based Features

  • Nurul Fadhilah Sulaiman
  • Mohd Zalisham Jali
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 362)

Abstract

SMS spamming is an activity of sending ‘unwanted messages’ through text messaging or other communication services; normally using mobile phones. Nowadays there are many methods for SMS spam detection, ranging from the list-based, statistical algorithm, IP-based and using machine learning. However, an optimum method for SMS spam detection is difficult to find due to issues of SMS length, battery and memory performances. Hoping to minimize the aforementioned problems, this paper introduces another detection variance that is based on common characters used when sending SMS (i.e. numbers and symbols), SMS length and keywords. To verify our work, the proposed features were stipulated into five different algorithms and then, tested with three different datasets for their ability to detect spam. From the conduct of experiments, it can be suggested that these three features are reasonable to be used for detecting SMS spam as it produced positive results. In the future, it is anticipated that the proposed algorithm will perform better when combined with machine learning techniques.

Keywords

Special Character Message Length Machine Learning Classifier Spam Detection Social Network Platform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Authors wish to thank the Ministry of Education (MOE), Malaysia for funding this research. The grant named RAGS with the grant code USIM/RAGS/FST/STH/36/50913.

References

  1. 1.
    Mujtaba, G., Yasin, M.: SMS spam detection using simple message content features. J. Basic Appl. Sci. Res. 4, 275–279 (2014)Google Scholar
  2. 2.
    Sohn, D-N., Lee, J-T., Rim, H-C.: The contribution of stylistic information to content-based mobile spam filtering. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 321–324 (2009)Google Scholar
  3. 3.
    Xu, Q., Xiang, E.W., Yang, Q.: SMS spam detection using non-content features. IEEE Intell. Syst. (2012)Google Scholar
  4. 4.
    Shahi, T.B., Yadav, A.: Mobile SMS spam filtering for Nepali text using naïve bayesian and support vector machine. Int. J. Intell. Sci. 4, 24–28 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhou, B., Yao Y., Luo J.: A three-way decision approach to email spam filtering. AI’10 Proceedings of the 23rd Canadian Conference on Advances in Artificial Intelligence, Vol. 6085, 28–39 (2010)Google Scholar
  6. 6.
    Nazirova, S.: Survey on spam filtering techniques. Commun. Netw. J. 3, 153–160 (2011)CrossRefGoogle Scholar
  7. 7.
    Mohammad, N.T.: A fuzzy clustering approach to filter spam e-mail. In: Proceedings of the World Congress on Engineering, vol. 1, 945 (2011)Google Scholar
  8. 8.
    Dasgupta, A., Gurevich, M., Punera, K.: Enhanced email spam filtering through combining similarity graphs. In: WSDM’11 Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 785–794 (2011)Google Scholar
  9. 9.
    Nosrati, L., Pour, A.N.: Dynamic concept drift detection for spam email filtering. In: Proceedings of ACEEE 2nd International Conference on Advances Information and Communication Technologies (ICT 2011), vol. 2 (2011) 124–126Google Scholar
  10. 10.
    Chakraborty, S., Mondal, B.: Spam mail filtering using different decision tree classifiers through data mining approach—a comparative performance analysis. Int. J. Comput. Appl. 47, 26–31 (2012)Google Scholar
  11. 11.
    Pour, A.N., Kholghi, R., Roudsari, B.: Minimizing the time of spam mail detection by relocating filtering system to the sender mail server. Int. J. Netw. Secur. Appl. 4, 53–62 (2012)Google Scholar
  12. 12.
    Ramachandran, A., Dagon, D., Feamster, N.: Can DNS-based blacklists keep up with bots?. CEAS 2006-Third Conference on Email and Anti-Spam (2006)Google Scholar
  13. 13.
    Levine, J.R.: Experience with greylisting. In: Proceedings of second conference on Email and Anti-Spam (CEAS 2005), pp. 1–2 (2005)Google Scholar
  14. 14.
    Amayri, O., Bouguila, N.: A study of spam filtering using support vector machines. J. Artif. Intell. Rev. 34, 73–108 (2010)CrossRefGoogle Scholar
  15. 15.
    Rouse, M.: Reverse DNS (rDNS) definition. (2007). http://searchnetworking.techtarget.com/definition/reverse-DNS. Accessed 01 March 2015
  16. 16.
    Tan, H., Goharian, N., Sherr, M.: $100,000 Prize Jackpot. Call Now! Identifying the Pertinent Features of SMS Spam. SIGIR’12 (2012)Google Scholar
  17. 17.
    Mosquare, A., Aouad, L., Grzonkowski, S., Morss, D.: On detecting messaging abuse in short text messages using linguistic and behavioural patterns (2014)Google Scholar
  18. 18.
    Bilal, J.M., Farooq, M.: Using evolutionary learning classifiers to do mobile spam (SMS) filtering. In: Proceeding GECCO’11 Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1795–1802 (2011)Google Scholar
  19. 19.
    Nuruzzaman, M.T., Lee, C., Choi, D.: Independent and personal SMS spam filtering. In Proceedings of IEEE Conference on Computer and Information Technology, pp. 429–435 (2011)Google Scholar
  20. 20.
    Almeida, T.A., GÃ3mez Hidalgo, J.M., Yamakami, A.: Contributions to the study of SMS spam filtering: new collection and results. In: Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG’11) (2011)Google Scholar
  21. 21.
    British English SMS Corpora. (2011). Downloaded from http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/. Accessed 07 Aug 2014
  22. 22.
    DIT SMS Spam Dataset. Dublin Institute of Technology (DIT). (2012). Downloaded from http://www.dit.ie/computing/research/resources/smsdata/. Accessed 05 Aug 2014

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Science and TechnologyUniversiti Sains Islam Malaysia (USIM)NilaiMalaysia

Personalised recommendations