Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN

Abstract

Smishing is a social engineering attack using SMS containing malicious content to deceive individuals into disclosing sensitive information or transferring money to cybercriminals. Smishing attacks have surged by $328 \%$, posing a major threat to mobile users, with losses exceeding ${\$}$54.2 million in 2019, yet the issue remains significantly under-addressed despite its growing prevalence. This paper presents a novel hybrid machine learning model for detecting Bangla smishing texts, combining Bidirectional Encoder Representations from Transformers (BERT) with Convolutional Neural Networks (CNNs) for enhanced characterlevel analysis. Our model addresses multi-class classification by distinguishing between Normal, Promotional, and Smishing SMS. Unlike traditional binary classification methods, our approach integrates BERT’s contextual embeddings with CNN’s characterlevel features, improving detection accuracy. Enhanced by an attention mechanism, the model effectively prioritizes crucial text segments. Our model achieves $98.47 \%$ accuracy, outperforming traditional classifiers, with high precision and recall in Smishing detection, and strong performance across all categories.