SMS spam detection using BERT and multi-graph convolutional networks
The surge in smartphone usage has significantly increased Short Message Service (SMS) traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches. Traditional rule-based and blacklist methods fail against evolving spamming techniques, prompting the adop...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-01-01
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| Series: | International Journal of Intelligent Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666603025000089 |
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| author | Linjie Shen Yanbin Wang Zhao Li Wenrui Ma |
| author_facet | Linjie Shen Yanbin Wang Zhao Li Wenrui Ma |
| author_sort | Linjie Shen |
| collection | DOAJ |
| description | The surge in smartphone usage has significantly increased Short Message Service (SMS) traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches. Traditional rule-based and blacklist methods fail against evolving spamming techniques, prompting the adoption of machine learning and deep learning approaches. However, models like Convolutional Neural Networks and Recurrent Neural Networks struggle to capture global co-occurrence patterns and complex semantics, while transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) lack explicit syntactic and co-occurrence modeling. To address these limitations, we propose the BERT with Triple-Graph Convolutional Networks (BERT-G3CN) model, the first framework to integrate BERT word embeddings with graph embeddings from Co-occurrence, Heterogeneous, and Integrated Syntactic Graphs. This multigraph approach captures diverse features and models both global and local structures using tailored Graph Convolutional Networks. Experiments on two benchmark datasets demonstrate that BERT-G3CN achieves superior accuracy of 99.28 % and 93.78 %, representing an improvement of approximately 2–3 % over competitive baselines. |
| format | Article |
| id | doaj-art-f5f1810a2c0d43fe8c0ec10a5b9c47e3 |
| institution | DOAJ |
| issn | 2666-6030 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Intelligent Networks |
| spelling | doaj-art-f5f1810a2c0d43fe8c0ec10a5b9c47e32025-08-20T02:47:36ZengKeAi Communications Co., Ltd.International Journal of Intelligent Networks2666-60302025-01-016798810.1016/j.ijin.2025.06.002SMS spam detection using BERT and multi-graph convolutional networksLinjie Shen0Yanbin Wang1Zhao Li2Wenrui Ma3School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, Zhejiang, ChinaDepartment of Engineering, Shenzhen MSU-BIT University, Shenzhen, 518172, Guangdong, ChinaZhejiang Lab, Hangzhou, Zhejiang, ChinaSchool of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, Zhejiang, China; Department of Big Data and Future E-Commerce Technology, Zhejiang Key Laboratory, Hangzhou, Zhejiang, China; Corresponding author. School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, Zhejiang, ChinaThe surge in smartphone usage has significantly increased Short Message Service (SMS) traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches. Traditional rule-based and blacklist methods fail against evolving spamming techniques, prompting the adoption of machine learning and deep learning approaches. However, models like Convolutional Neural Networks and Recurrent Neural Networks struggle to capture global co-occurrence patterns and complex semantics, while transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) lack explicit syntactic and co-occurrence modeling. To address these limitations, we propose the BERT with Triple-Graph Convolutional Networks (BERT-G3CN) model, the first framework to integrate BERT word embeddings with graph embeddings from Co-occurrence, Heterogeneous, and Integrated Syntactic Graphs. This multigraph approach captures diverse features and models both global and local structures using tailored Graph Convolutional Networks. Experiments on two benchmark datasets demonstrate that BERT-G3CN achieves superior accuracy of 99.28 % and 93.78 %, representing an improvement of approximately 2–3 % over competitive baselines.http://www.sciencedirect.com/science/article/pii/S2666603025000089SMS spam detectionBERTGraph convolutional networks |
| spellingShingle | Linjie Shen Yanbin Wang Zhao Li Wenrui Ma SMS spam detection using BERT and multi-graph convolutional networks International Journal of Intelligent Networks SMS spam detection BERT Graph convolutional networks |
| title | SMS spam detection using BERT and multi-graph convolutional networks |
| title_full | SMS spam detection using BERT and multi-graph convolutional networks |
| title_fullStr | SMS spam detection using BERT and multi-graph convolutional networks |
| title_full_unstemmed | SMS spam detection using BERT and multi-graph convolutional networks |
| title_short | SMS spam detection using BERT and multi-graph convolutional networks |
| title_sort | sms spam detection using bert and multi graph convolutional networks |
| topic | SMS spam detection BERT Graph convolutional networks |
| url | http://www.sciencedirect.com/science/article/pii/S2666603025000089 |
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