SMS Spam Detection Using Machine Learning and Deep Learning Techniques

SMS Spam Detection Using Machine Learning and Deep Learning Techniques

Pooja Kapila1 (Guide), Alok Kumar2, Neeraj Sharma3, Mayank Maurya4

1234Department of Artificial Intelligence And Data Science

1234IIMT College of Engineering, Greater Noida, UP, India

alok1602.kumar@gmail.com, ns5647871@gmail.com , mayankmaurya2468@gmail.com

Abstract –

The rapid development of mobile technology has brought with it the challenge of dealing with SMS spam, which has become a major concern for users’ privacy and telecommunication systems within networks. Apart from causing inconveniences, SMS spam can also lead to phishing, financial fraud, and even the spread of malware. This paper focuses on recent studies on SMS spam detection that utilized Machine Learning (ML) and Deep Learning (DL) technologies. Focus is given to model selection, dataset compilation, preprocessing steps, evaluation benchmarks, and explainability for performance assessment. The most accurate models are the state-of-the-art hybrid deep and transformer-based models because of their flexibility in capturing complex patterns within the text, although traditional ML approaches are still applicable for resource-constrained, lightweight deployments.

Keywords: Spam SMS, Machine Learning, Deep Learning, NLP, Transformers, CNN, LSTM, Explainable AI, Spam Filtering..