Enhancing SMS Spam Detection using Deep Learning Techniques

Giri Moorthy, Tamil Selvan (2024) Enhancing SMS Spam Detection using Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

SMS spam is a major problem in mobile communication which will cause issues like financial loss and privacy violations. SMS spam detection is used to identify unwanted messages, protecting users from scams. The primary goal of SMS spam detection is to identify the difference between legitimate (ham) and unwanted messages(spam). Many models have been used to detect spam, but due to advancements in spam techniques we needed better detection methods to identify the advanced spam messages. Traditional machine learning models like Random Forest and deep learning models such as RNN, LSTM, BI-LSTM, and GRU have worked well in identifying spam messages, but it fails to understand the deeper meaning of the message. This research focuses on identifying these issues by using advanced Transformer models like Bert to compare whether these models will perform better than the traditional methods and other deep learning models. This research is very important because due to the rise in technology development spam messages are more sophisticated to identify, so we need powerful and accurate detection models to detect them. People use their mobile a lot for communication, transferring information and protecting them from unwanted messages will improve their security and user experience. Transformer models like Bert are good in understanding the deeper meaning of the word, so this research identify whether these models can expect to do a better performance compared to other methods in detecting SMS Spam.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > Electronic Mail T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > Electronic Mail Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 11:37
Last Modified:	02 Sep 2025 11:37
URI:	https://norma.ncirl.ie/id/eprint/8700

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