Development of SMS Spam Filtering APP for Modern Mobile Devices

Short Messaging Service (SMS) spam has been known to be the unwanted or unintended messages received on mobile phones. This paper has presented a review of current methods, existing problems, and future research directions on spam classification techniques of mobile SMS spams. The methodology involves collecting a large dataset of SMS messages, both legitimate and spam, to train and evaluate various machine learning algorithms. Feature extraction techniques have been employed to capture relevant information from SMS messages, such as the presence of specific keywords, the length of message, and the sender's identity. The experimental results on  the proposed spam filtering system achieves a high level of accuracy with a low false-positive rate, thereby minimizing the chances of legitimate messages being classified as spam. The system effectively detects and blocks a significant portion of spam messages, providing mobile users with a reliable defense against unwanted SMS communications. The findings of this study reveal that machine learning algorithms, particularly ensemble methods like Random Forests, performed well in SMS spam filtering on mobile devices. 

[1]   Al-Hasan A.A., El-Alfy E.-S.M. (2019) Dendritic cell algorithm for mobile phone spam filtering, Procedia Computer Science  52244-251.

[2]  Chan, P.P., Yang, C., Yeung, D.S. and Ng, W.W. (2019) "Spam filtering for short messages in adversarial environment", Neurocomputing, Vol. 155, 167-176.

[3]  Choudhary, N. and Jain, A.K. (2019) "Towards filtering of SMS spam messages using machine learning based technique", in International Conference on Advanced Informatics for Computing Research, Springer., 18-30.

[4]  El-Alfy, E.-S.M. and AlHasan, A.A. (2019) "Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm", Future Generation Computer Systems, Vol. 64, 98-107.

[5]  G?mez-Adorno, H., Pinto, D., Sidorov, G., & Villase?or-Pineda, L. (2017). A linguistic approach and ensemble methods for SMS spam detection. Expert Systems with Applications, 68, 96-109.

[6]  Junaid, M.B. and Farooq, M. (2019) "Using evolutionary learning classifiers to do mobilespam (SMS) filtering", in Proceedings of the 13th annual conference on Genetic and evolutionary computation, 1795-1802.

[7]  Li, B., Zhang, B., & Lee, W. C. (2020). SMS spam filtering based on keywords and spammers: A multi-view classification approach. Expert Systems with Applications, 39(10), 9229-9236.

[8] Pham, D. T., Dang, T. D., & Nguyen, L. H. (2018). SMS spam filtering on mobile devices using machine learning techniques. In Proceedings of the International Conference on Advanced Computational Intelligence (ICACI) (pp. 435-440).

[9]  Santos, M. F., Cardoso, J. S., & Oliveira, H. P. (2021). Combining rule-based and machine learning classifiers for SMS spam filtering. Expert Systems with Applications, 41(4), 1933-1943.

[10]                      Serrano, J.M.B., Palancar, J.H. and Cumplido, R. (2019­) "The evaluation of ordered features for SMS spam filtering", in Iberoamerican Congress on Pattern Recognition, Springer., 383-390.

[11]                       Suleiman, D. and Al-Naymat, G. (2017) "SMS spam detection using h2o framework", Procedia Computer Science, Vol. 113, 154-161.

[12]                      Uysal, A.K., Gunal, S., Ergin, S. and Gunal, E.S., (2019) "A novel framework for SMS spam filtering", in 2012 International Symposium on Innovations in Intelligent Systems and Applications, IEEE., 1-4.

[13]                      Yadav, K., Saha, S., Kumaraguru, P., Kumra, R.. (2020) “Take control of your SMSes: Designing an usable spam SMS filtering system”, IEEE 13th International Conference on Mobile Data Management, MDM.

[14]                      Top of FormZainal, K., Sulaiman, N. and Jali, M., "An analysis of various algorithms for text spam classification and clustering using rapidminer and weka", International Journal of Computer Science and Information Security, Vol. 13, No. 3, (2022), 66-77.