用BERT+字符CNN检测孟加拉语诈骗短信，准确率超98%。
Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN
- 融合BERT上下文特征与字符级CNN，提升文本分析能力
- 在正常、促销、诈骗三类短信中达98.47%准确率
- 适合反诈系统开发与南亚语言安全研究者使用
短信诈骗（Smishing）是一种通过含恶意内容的短信欺骗用户泄露敏感信息或转账的社交工程攻击。2019年全球损失超过5420万美元，攻击数量飙升328%，但相关研究仍严重不足。本文提出一种新型混合机器学习模型，结合双向编码器表示（BERT）与卷积神经网络（CNN），用于检测孟加拉语短信中的诈骗内容。该模型实现多分类任务，区分正常、促销和诈骗短信三类。不同于传统二分类方法，本方案融合BERT的上下文嵌入与CNN的字符级特征，利用注意力机制强化关键文本片段识别。实验表明，模型整体准确率达98.47%，在诈骗短信检测上兼具高精确率与高召回率，各类别表现均优异。
原文摘要 · Abstract (English)
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.
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