Benchmarking Classical and Deep Learning Models for Cyberbullying, Spam, and Smishing Detection

Abstract

SMS spam and smishing detection is an important problem that arise in automatic message filtering systems. Already existing solutions make the use of complex deep learning models that act as black boxes. In this work, we provide a combined comparison of classical machine learning models, ensemble methods as well as deep learning approaches for classifying SMS messages mainly into three categories: ham, spam, and smishing. Classical and ensemble models use TF-IDF features to convert text into numeric vectors, whereas deep models include BiLSTM and a transformer-based BERT for semantic representation learning. The experiments are conducted on a balanced public dataset using the same preprocessing steps and evaluation process for all models to ensure a fair comparison. Both the neural models are trained for an identical number of epochs so that their comparison is not affected due to unequal training budgets. The models used in this study include Logistic Regression, Multinomial Naïve Bayes, Linear SVM, Random Forest, XGBoost, BiLSTM, and BERT. Results show that all the proposed models exhibit high classification performance, with BiLSTM having the maximum accuracy of 98.19% which is closely followed by BERT (98.14%), Linear SVM (98.04%), and Random Forest (97.99%), while simpler models remain competitive, highlighting the trade-offs between accuracy, interpretability, and computational cost. Under an equal-epoch training budget, BiLSTM and BERT achieve nearly similar performance, indicating that the two architectures are almost evenly matched. This study provides useful practical insights for building effective and efficient SMS spam and smishing detection systems.