A Comparative Analysis of SMS Spam Detection employing Machine Learning Methods
Humaira Yasmin Aliza, Kazi Aahala Nagary, Eshtiak Ahmed, Kazi Mumtahina Puspita, Khadiza Akter Rimi, Ankit Khater, Fahim Faisal
Abstract
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.