Abstract: The popularity of mobile devices is increasing day by day as they provide a large variety of services by reducing the cost of services. Short Message Service (SMS) is considered one of the widely used communication service. But this has also resulted in a rise in attacks on mobile devices, such as SMS spam. In this study, we suggest an innovative machine learning spam message detection and filtering technique based on classification algorithms. After a careful examination of the characteristics of spam messages, ten parameters were found to be useful in distinguishing SMS spam messages from ham messages. When our recommended method was applied, the Random Forest classification algorithm produced a 1.02% false positive rate and a 96.5% true positive rate.
Keywords: SMS spam, Mobile devices, Machine learning, Feature Selection
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