Intelligent SMS Filtering: Leveraging Machine Learning Algorithms for Spam Detection

Security risks and incoherent presentations bring proliferation of spam messages to be a problem toward effective communication.Here, the contribution is a new approach in SMS spam detection, based on exploiting different learning machine algorithms, testing several classification approaches intended to distinguish between real messages and spam using Support Vector Machines, Random Forests, and Neural Networks.Using this above proposed model, a huge dataset consisting of many SMS samples were trained on it, and their testing was pretty tough with accuracy, precision, recall, and F1-score performance measures used.Results Here, the developed machine learning based SMS spam detection system has proven its superiority in spam detection by results that show a good reduction in the number of spam SMS messages to users as well as service providers.The study clearly shows the importance of feature selection and model optimization in achieving high detection rates.Last, we have some implications of our work for future development in SMS security as well as the potential to integrate these techniques into mobile applications.