This paper introduces an accurate smishing filtering model named S-Defender, designed to detect smishing messages. The model analyzes the text message content and employs a Naive Bayesian classifier to categorize the message as either smishing or ham. Additionally, our approach normalizes and converts Lingo language text messages into their standard form to improve classification accuracy. The model’s validation, conducted using the English SMS dataset, resulted in an overall accuracy of 94.72%.