A survey of Emerging Techniques in Detecting SMS Spam


  • Sahar Alqahtani King Abdulaziz University
  • Daniyal Alghazzawi Department of Information Systems, Faculty of Computing and Information Technology at King Abdulaziz University, Jeddah, Saudi Arabia




In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.


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How to Cite

Alqahtani, S., & Alghazzawi, D. . (2019). A survey of Emerging Techniques in Detecting SMS Spam . Transactions on Engineering and Computing Sciences, 7(5), 23–35. https://doi.org/10.14738/tmlai.75.7116