SMS Spam Detection System Based on Deep Learning Architectures in Turkish and English Messages
Zafer Albayrak, Hakan Can Altunay · 2023
SMS still continues its existence despite the emergence of different messaging services. It takes a part in our lives as a communication service to date. Companies use SMS for advertisement purposes due to the fact that e-mail filtering systems have rooted, short message systems are being undersold by the operators, and spam detection and blocking systems used for short messages are ineffective. Consequently, SMS has attracted the attention of those who send spam to mobile phone users. This article presents the proposal of a hybrid model with the aim of detecting SMS spam messages. This detection model uses GRU and CNN as two deep learning methods. The design for this model was laid out by using two different datasets containing combined text messages written in the Turkish and English languages. The testing process was performed on the dataset through benchmarking as well as other machine learning algorithms. It was revealed in the study that the hybrid CNN+GRU approach attained an accuracy of 99.12% by demonstrating a better performance compared to the other algorithms.