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

TL;DR

This paper introduces a hybrid BERT and CNN-based machine learning model that effectively detects Bangla smishing texts, significantly improving accuracy over traditional methods and addressing multi-class SMS classification.

Contribution

The paper presents a novel hybrid model combining BERT and character-level CNNs with attention for multi-class smishing detection in Bangla, enhancing accuracy and robustness.

Findings

Achieved 98.47% detection accuracy

Outperformed traditional classifiers in precision and recall

Effective multi-class classification of SMS types

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. Despite its growing prevalence, the issue remains significantly under-addressed. 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 character-level 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 character-level 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.