Development of SMS Spam Filtering App for Modern Mobile Devices
Abstract
Short Messaging Service 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, perform well in SMS spam filtering on mobile devices.
References
Al-Hasan A.A., & El-Alfy E.-S.M. (2019). Dendritic cell algorithm for mobile phone spam filtering, Procedia Computer Science 52244-251.
Chan, P.P., Yang, C., Yeung, D.S., & Ng, W.W. (2019). Spam filtering for short messages in adversarial environment, Neurocomputing, Vol. 155, 167-176.
Choudhary, N., & 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.
El-Alfy, E.-S.M., & Al-Hasan, A.A. (2019). Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm, Future Generation Computer Systems, Vol. 64, 98-107.
Gómez-Adorno, H., Pinto, D., Sidorov, G., & Villaseñor-Pineda, L. (2017). A linguistic approach and ensemble methods for SMS spam detection. Expert Systems with Applications, 68, 96-109.
Junaid, M.B., & 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.
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.
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).
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.
Serrano, J.M.B., Palancar, J.H., & Cumplido, R. (2019¬). The evaluation of ordered features for SMS spam filtering, in Iberoamerican Congress on Pattern Recognition, Springer., 383-390.
Suleiman, D., & Al-Naymat, G. (2017). SMS spam detection using h2o framework, Procedia Computer Science, Vol. 113, 154-161.
Uysal, A.K., Gunal, S., Ergin, S., & 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.
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.
Zainal, K., Sulaiman, N. & Jali, M. (2022). 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.
Copyright (c) 2024 Journal of Computing and Social Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarise, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and reference publication in the Journal.
2) For open access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds copyright, or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subject to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) is/are responsible to ensure his or her or their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. Corresponding author(s) has (have) obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If submitted manuscript includes materials from others, the authors have obtained the permission from the copyright owners.
5) In signing this statement, the author(s) declare(s) that the researches in which they have conducted are in compliance with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving human or the use of animal samples must obtain approval from Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that he or she has no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor or UNIMAS Publisher.
