SpamLLM: Leveraging Large Language Models for Robust Spam Email Classification.
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| Title: | SpamLLM: Leveraging Large Language Models for Robust Spam Email Classification. |
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| Authors: | He, Zhiyong1 (AUTHOR) 2012100740@niit.edu.cn, Liu, Chang2 (AUTHOR), Hu, Boyang3 (AUTHOR), Jia, Duo4 (AUTHOR), Murray, Richard (AUTHOR) rmurray@wiley.com |
| Source: | International Journal of Intelligent Systems. 6/23/2026, Vol. 2026, p1-17. 17p. |
| Subjects: | Spam filtering (Email), Language models, Spam email, Latent semantic analysis, Email security, Transformer models, Machine learning |
| Abstract: | Spam email detection remains an ongoing challenge due to the increasing sophistication and evolving tactics employed by spammers. Traditional rule–based and machine learning (ML) detection methods have demonstrated limitations in adaptability and generalization. This paper proposes SpamLLM, a multimodal spam detection framework that leverages a frozen large language model (LLM) backbone integrated with semantic embeddings and structured metadata extracted from email headers and body statistics. Rigorous evaluations are conducted across four widely used spam detection datasets, and SpamLLM is compared against classical ML, deep learning, and pretrained Transformer‐based baseline models. The experimental results demonstrate that SpamLLM outperforms existing methods and achieves state‐of‐the‐art performance in accuracy, precision, recall, and F1 score. Notably, SpamLLM excels in handling diverse spam content and provides robust detection across various datasets. These findings underscore the potential of multimodal fusion approaches for advancing spam classification systems and suggest promising directions for future research in email security. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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