A Spam Transformer Model for SMS Spam Detection

• Main Authors: Xiaoxu Liu, Haoye Lu, Amiya Nayak
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: SMS spam detection; transformer; attention; deep learning
• Online Access: https://ieeexplore.ieee.org/document/9433507/

In this paper, we aim to explore the possibility of the Transformer model in detecting the spam Short Message Service (SMS) messages by proposing a modified Transformer model that is designed for detecting SMS spam messages. The evaluation of our proposed spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl’s Twitter Spam Detection Competition dataset, with the benchmark of multiple established machine learning classifiers and state-of-the-art SMS spam detection approaches. In comparison to all other candidates, our experiments on SMS spam detection show that the proposed modified spam Transformer has the optimal results on the accuracy, recall, and F1-Score with the values of 98.92%, 0.9451, and 0.9613, respectively. Besides, the proposed model also achieves good performance on the UtkMl’s Twitter dataset, which indicates a promising possibility of adapting the model to other similar problems.