Machine Learning in the Application of Fraudulent SMS Filtering

Electronic fraud SMS is a common network security threat, which brings huge economic and privacy risks to users. To effectively address this problem, this paper studies the electronic scam SMS filtering method based on machine learning. By analyzing and processing a large number of SMS datasets, we propose a classification model that comprehensively considers both textual and behavioral features to identify and filter e-scam text messages. The experimental results show that our method achieves significant improvements in accuracy and efficiency and provides better security for users.