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 research, we propose a unique machine learning classification algorithm-based spam message detection and filtering method. Ten factors that can effectively separate SMS spam messages from ham messages were discovered after a thorough analysis of the traits of spam messages. The Random Forest classification technique yielded a 1.02% false positive rate and a 96.5% true positive rate when using our suggested approach.

Keywords: SMS spam, Mobile devices, Machine learning, Feature Selection.

| DOI: 10.17148/IARJSET.2024.11440