A survey of Emerging Techniques in Detecting SMS Spam

Abstract

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.

Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

“…In [4], the authors present a review to compare the performance of machine learning algorithms. The review proves that the use of support vector machine (SVM) and Naive Bayes leads to efficient performance.…”

“…Privacy invasion and access to sensitive or unauthorized information are the main problems arising from spam messages. The privacy of the user is violated by individuals commonly known as spammers, who use various unethical activities to access user data stored on smartphones without the knowledge of the user [4].…”