SMS Spam Filtering on Multiple Background Datasets Using Machine Learning Techniques: A Novel Approach

Abstract

Short Message Service (SMS) is one of the well-known and reliable communication services in which a message sends electronically. In the current era, the declining in the cost per SMS day by day by overall all the telecom organizations in India has encouraged the extended utilization of SMS. This ascent pulled in assailants, which have brought about SMS Spam problem. Spam messages include advertisements, free services, promotions and marketing, awards, etc. Individuals are utilizing the ubiquity of cell phone gadgets is growing day by day as telecom giants give a vast variety of new and existing services by reducing the cost of all services. Short Message Service (SMS) is one of the broadly utilized communication services. Due to the high demand for SMS service, it has prompted a growth in mobile phones attacks like SMS Spam. In our proposed approach, we have presented a general model that can distinguish and filter the spam messages utilizing some existing machine learning classification algorithms. Our approach builds a generalized SMS spam-filtering model, which can filter messages from various backgrounds (Singapore, American, Indian English etc.). In our approach, preliminary results are mentioned below based on Singapore and Indian English based publicly available datasets. Our approach showed promise to accomplish a high precision utilizing Indian English SMS large datasets and others background's datasets also.