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Developing a Messaging Application that Filters SMS Spam Messages Using Machine Learning
Bashehab, Mayan Alanood, Alhuttami Layal, Alsilani
Bashehab, Mayan
Alanood, Alhuttami
Layal, Alsilani
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Abstract
The increasing prevalence of SMS spam poses a significant challenge as mobile devices have become integral to daily life. Spam messages target individuals and can lead to high impacts, as they can evolve into phishing attacks or even social engineering. Our project holds significance in contributing to the safety of the community by reducing the number of spam victims. It aims to improve existing spam filtering solutions to provide a safer and more user-friendly experience, thereby reducing the vulnerability of users to spam and phishing attacks. Through state-of-the-art analysis, we identified a common gap: the lack of real-world application testing. In our methodology, we utilized the TensorFlow Lite Model Maker library to simplify the adaptation and deployment of an NLP neural network model text classification model on mobile devices. Our result is a meticulously designed user-friendly application that flags spam messages in real-time, o ering a safer messaging experience.