Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916915436322816 |
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| author | Tanbhir, Gazi Shahriyar, Md. Farhan Shahed, Khandker Chy, Abdullah Md Raihan Adnan, Md Al |
| author_facet | Tanbhir, Gazi Shahriyar, Md. Farhan Shahed, Khandker Chy, Abdullah Md Raihan Adnan, Md Al |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN Tanbhir, Gazi Shahriyar, Md. Farhan Shahed, Khandker Chy, Abdullah Md Raihan Adnan, Md Al Computation and Language Machine Learning Social and Information Networks 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. |
| title | Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN |
| topic | Computation and Language Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2502.01518 |