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Destek Vektör Makineleri Kullanılarak Spam SMS Tespiti

Year 2019, Volume: 22 Issue: 3, 779 - 784, 01.09.2019
https://doi.org/10.2339/politeknik.429707

Abstract

Kısa Mesaj Servisi (SMS) son yılların en önemli iletişim araçlarından biri haline gelmiştir. Mobil cihazların artan popülaritesiyle, SMS kullanım oranları da yıllar içinde daha da artmaya devam edecektir. SMS doğrudan bireylere ulaşmak için kullanılan pratik bir yöntem olarak kullanılmaktadır. Ancak bu pratik ve kolay yöntem, SMS'in yanlış ve kötü amaçlı kullanılmasına da neden olabilmektedir. Şirketlerin reklam veya tanıtım SMS'leri bu yanlış kullanımın önemli bir örneğidir. Bu çalışmada, Destek Vektör Makineleri kullanılarak bir spam SMS tespit tekniği önerilmiştir. Bu çalışmada 747 spam SMS ve 4827 jambon SMS içeren SMSSpamCollection veri kümesi kullanılmıştır. Veri kümesindeki Spam SMS tahminini değerlendirmek için 10 katlı çapraz doğrulama tekniği kullanılmıştır. Önerilen yaklaşımda Destek Vektör Makineleri sınıflandırma algoritması ile %98.33 oranında başarılı tespit yapılarak, 0,087 yanlış pozitif oran elde etmiştir.

References

  • 1. Choudhary, N., & Jain, A. K., Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique. In Advanced Informatics for Computing Research pp. 18-30, Springer, Singapore, 2017.
  • 2. Mujtaba, G., & Yasin, M., SMS spam detection using simple message content features. J. Basic Appl. Sci. Res, 4(4), 275-279, 2014.
  • 3. 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.
  • 4. 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.
  • 5. 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.
  • 6. Yadav, K., Kumaraguru, P., Goyal, A., Gupta, A., Naik, V.: SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering. 12th Workshop on Mobile Computing Systems and Applications, pp. 1–6. ACM 2011.
  • 7. 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.
  • 8. G. Chechik, G. Heitz Max-margin Classification of Data with Absent Futures. In Journal of Machine Learning Research 9, 2008.
  • 9. Osowski, S., Siwekand, K., and Markiewicz, T. MLP and SVM Networks – a Comparative Study. Proceedings of the 6th Nordic Signal Processing Symposium – NORSIG, 2004.
  • 10. Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the Study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11), Mountain View, CA, USA, 2011.

Support Vector Machine Based Spam SMS Detection

Year 2019, Volume: 22 Issue: 3, 779 - 784, 01.09.2019
https://doi.org/10.2339/politeknik.429707

Abstract

Short Message Service (SMS) is the most important communication tool in recent decades. With the increased popularity of mobile devices, the usage rate of SMS will increase more and more in years. SMS is a practical method used to reach individuals directly. But this practical and easy method can cause SMS to be misused. The advertising or promotional SMS of the companies are an examples of this misuse. In this study, a spam SMS detection technique is proposed using SVM. SMSSpamCollection dataset, which is contain 747 spam SMS and 4827 ham SMS, is used. 10 fold cross-validation technique is used to evaluate prediction of Spam SMS in the dataset. Therefore, proposed approach achieved 98.33 % true positive rate and 0,087 false positive rate for SVM classification algorithm.

References

  • 1. Choudhary, N., & Jain, A. K., Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique. In Advanced Informatics for Computing Research pp. 18-30, Springer, Singapore, 2017.
  • 2. Mujtaba, G., & Yasin, M., SMS spam detection using simple message content features. J. Basic Appl. Sci. Res, 4(4), 275-279, 2014.
  • 3. 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.
  • 4. 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.
  • 5. 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.
  • 6. Yadav, K., Kumaraguru, P., Goyal, A., Gupta, A., Naik, V.: SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering. 12th Workshop on Mobile Computing Systems and Applications, pp. 1–6. ACM 2011.
  • 7. 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.
  • 8. G. Chechik, G. Heitz Max-margin Classification of Data with Absent Futures. In Journal of Machine Learning Research 9, 2008.
  • 9. Osowski, S., Siwekand, K., and Markiewicz, T. MLP and SVM Networks – a Comparative Study. Proceedings of the 6th Nordic Signal Processing Symposium – NORSIG, 2004.
  • 10. Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the Study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11), Mountain View, CA, USA, 2011.
There are 10 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Adem Tekerek 0000-0002-0880-7955

Publication Date September 1, 2019
Submission Date June 1, 2018
Published in Issue Year 2019 Volume: 22 Issue: 3

Cite

APA Tekerek, A. (2019). Support Vector Machine Based Spam SMS Detection. Politeknik Dergisi, 22(3), 779-784. https://doi.org/10.2339/politeknik.429707
AMA Tekerek A. Support Vector Machine Based Spam SMS Detection. Politeknik Dergisi. September 2019;22(3):779-784. doi:10.2339/politeknik.429707
Chicago Tekerek, Adem. “Support Vector Machine Based Spam SMS Detection”. Politeknik Dergisi 22, no. 3 (September 2019): 779-84. https://doi.org/10.2339/politeknik.429707.
EndNote Tekerek A (September 1, 2019) Support Vector Machine Based Spam SMS Detection. Politeknik Dergisi 22 3 779–784.
IEEE A. Tekerek, “Support Vector Machine Based Spam SMS Detection”, Politeknik Dergisi, vol. 22, no. 3, pp. 779–784, 2019, doi: 10.2339/politeknik.429707.
ISNAD Tekerek, Adem. “Support Vector Machine Based Spam SMS Detection”. Politeknik Dergisi 22/3 (September 2019), 779-784. https://doi.org/10.2339/politeknik.429707.
JAMA Tekerek A. Support Vector Machine Based Spam SMS Detection. Politeknik Dergisi. 2019;22:779–784.
MLA Tekerek, Adem. “Support Vector Machine Based Spam SMS Detection”. Politeknik Dergisi, vol. 22, no. 3, 2019, pp. 779-84, doi:10.2339/politeknik.429707.
Vancouver Tekerek A. Support Vector Machine Based Spam SMS Detection. Politeknik Dergisi. 2019;22(3):779-84.