Fraudulent SMS identification based on enhanced BERT
Yanlong Shu, Hao Li · 2022
With the popularity of SMS services, telecommunication fraud incidents occur frequently, which seriously threaten people's life and property safety. The existing fraudulent SMS recognition techniques have the problems of feature selection relying on manual and low recognition. To solve this problem, a fraudulent SMS identification method (referred to as BERT-SIG) that combines BERT (bidirectional encoder representation from transformers) and Sigmoid is designed. The method performs automatic feature extraction by multi-headed attention mechanism, improves the quality of recognition by Sigmoid activation function, and performs the task of recognizing fraudulent SMS by fine-tuning to improve the efficiency of fraudulent SMS recognition. In the experiments, two open-source datasets, "Spam Email" and "E-Mail classification NLP", are used to check the persistence and robustness, and the BERT-SIG is compared with various machine learning models. The experimental results show that BERT-SIG has the best performance accuracy and F1 score, reaching 99.72% and 99.53% respectively, and can effectively identify fraudulent SMS.