Scopus Indexed Publications

Paper Details

A Comparative Analysis of SMS Spam Detection employing Machine Learning Methods
Humaira Yasmin Aliza, Fahad Faisal, KAZI AAHALA NAGARY, KHADIZA AKTER RIMI , Kazi Mumtahina Puspita,

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Random Forest, Logistic Regression and some more are performed. The result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research.

Support vector machines , Machine learning algorithms , Social networking (online) , Mobile handsets , Security , Reliability , Computer crime
Journal or Conference Name
Proceedings - 6th International Conference on Computing Methodologies and Communication, ICCMC 2022
Publication Year