Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique

  • Neelam ChoudharyEmail author
  • Ankit Kumar Jain
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 712)


The popularity of mobile devices is increasing day by day as they provide a large variety of services by reducing the cost of services. Short Message Service (SMS) is considered one of the widely used communication service. However, this has led to an increase in mobile devices attacks like SMS Spam. In this paper, we present a novel approach that can detect and filter the spam messages using machine learning classification algorithms. We study the characteristics of spam messages in depth and then found ten features, which can efficiently filter SMS spam messages from ham messages. Our proposed approach achieved 96.5% true positive rate and 1.02% false positive rate for Random Forest classification algorithm.


SMS spam Mobile devices Machine learning Feature selection 


  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Puniškis, D., Laurutis, R., Dirmeikis, R.: An artificial neural nets for spam e-mail recognition. Elektronika ir Elektrotechnika 69, 73–76 (2006)Google Scholar
  6. 6.
    Jain, A.K., Gupta, B.B.: Phishing detection: analysis of visual similarity based approaches. Secur. Commun. Netw. 2017 (2017). Article ID 5421046. doi: 10.1155/2017/5421046
  7. 7.
    Gupta, B.B., Tewari, A., Jain, A.K., Agrawal, D.P.: Fighting against phishing attacks: state of the art and future challenges. Neural Comput. Appl. 1–26 (2016). doi: 10.1007/s00521-016-2275-y
  8. 8.
    Jain, A.K., Gupta, B.B.: A novel approach to protect against phishing attacks at client side using auto-updated white-list. EURASIP J. Inf. Secur. 1–11 (2016). doi: 10.1186/s13635-016-0034-3
  9. 9.
    Choudhary, N., Jain, A.K.: Comparative Analysis of Mobile Phishing Detection and Prevention Approaches (Accepted)Google Scholar
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
  15. 15.
    El-Alfy, E.S.M., AlHasan, A.A.: Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm. Future Gen. Comput. Syst. 64, 98–107 (2016). doi: 10.1016/j.future.2016.02.018 CrossRefGoogle Scholar
  16. 16.
    Jialin, M., Zhang, Y., Liu, J., Yu, K., Wang, X.: Intelligent SMS spam filtering using topic model. In: International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 380–383. IEEE (2016). doi: 10.1109/INCoS.2016.47
  17. 17.
    Chan, P.P.K., Yang, C., Yeung, D.S., Ng, W.W.Y.: Spam filtering for short messages in adversarial environment. Neurocomputing 155, 167–176 (2015). doi: 10.1016/j.neucom.2014.12.034
  18. 18.
    Delany, S.J., Buckley, M., Greene, D.: SMS spam filtering: methods and data. Expert Syst. Appl. 39, 9899–9908 (2012). doi: 10.1016/j.eswa.2012.02.053
  19. 19.
    Xu, Q., Xiang, E.W., Yang, Q., Du, J., Zhong, J.: SMS spam detection using non-content features. IEEE Intell. Syst. 27(6), 44–51 (2012)CrossRefGoogle Scholar
  20. 20.
    Nuruzzaman, M.T., Lee, C., Abdullah, M., Choi, D.: Simple SMS spam filtering on independent mobile phone. Secur. Commun. Netw. 1209–1220 (2012). doi: 10.1002/sec.577
  21. 21.
    Uysal, A.K., Gunal, S., Ergin, S., Gunal, E.S.: A novel framework for SMS spam filtering. In: International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–4. IEEE (2012). doi: 10.1109/INISTA.2012.6246947
  22. 22.
    Yadav, K., Kumaraguru, P., Goyal, A., Gupta, A., Naik, V.: SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering. In: 12th Workshop on Mobile Computing Systems and Applications, pp. 1–6. ACM (2011). doi: 10.1145/2184489.2184491
  23. 23.
    Hidalgo, J.M.G., Bringas, G.C., Sánz, E.P., García, F.C.: Content based SMS spam filtering. In: ACM Symposium on Document Engineering, pp. 107–114. ACM (2006). doi: 10.1145/1166160.1166191
  24. 24.
  25. 25.
    Cormack, G.V., Hidalgo, J.M.G., Sánz, E.P.: Feature engineering for mobile (SMS) spam filtering. In: 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 871–872. ACM (2007). doi: 10.1145/1277741.1277951
  26. 26.
    Cormack, G.V., Hidalgo, J.M.G., Sánz, E.P.: Spam filtering for short messages. In: 16th ACM Conference on Conference on Information and Knowledge Management, pp. 313–320. ACM (2007). doi: 10.1145/1321440.1321486
  27. 27.
    Ayodele, T.O.: Types of machine learning algorithms. In: New Advances in Machine Learning. INTECH Publisher (2010)Google Scholar
  28. 28.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Computer Engineering DepartmentNational Institute of TechnologyKurukshetraIndia

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