SMS Spam Filtering Using Machine Learning Techniques: A Survey

Objective: To report a review of various machine learning and hybrid algorithms for detecting SMS spam messages and comparing them according to accuracy criterion. Data sources: Original articles written in English found in Sciencedirect.com, Google-scholar.com, Search.com, IEEE explorer, and the ACM library. Study selection: Those articles dealing with machine learning and hybrid approaches for SMS spam filtering. Data extraction: Many articles extracted by searching a predefined string and the outcome was reviewed by one author and checked by the second. The primary paper was reviewed and edited by the third author. Results: A total of 44 articles were selected which were concerned machine learning and hybrid methods for detecting SMS spam messages. 28 methods and algorithms were extracted from these papers and studied and finally 15 algorithms among them have been compared in one table according to their accuracy, strengths, and weaknesses in detecting spam messages of the Tiago dataset of spam message. Actually, among the proposed methods DCA algorithm, the large cellular network method and graph-based KNN are three most accurate in filtering SMS spams of Tiago data set. Moreover, Hybrid methods are discussed in this paper.