A compound-aware encoder-only transformer model for smishing detection.
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| Title: | A compound-aware encoder-only transformer model for smishing detection. |
|---|---|
| Authors: | Nguyen-Xuan, Sam1 (AUTHOR) samnx2@fe.edu.vn, Nguyen, Han2 (AUTHOR) han316@usf.edu |
| Source: | Journal of Supercomputing. Mar2026, Vol. 82 Issue 4, p1-25. 25p. |
| Subjects: | Transformer models, Mobile communication system security, Machine learning, Text mining, Encoding, Phishing |
| Abstract: | Smishing attacks increasingly exploit lexical obfuscation and compound-word constructions within both SMS content and embedded URLs, which significantly degrades the effectiveness of conventional tokenization-based detection approaches. To address this challenge, we propose the Compound-Aware Encoder-Only Transformer (CAEoT), a framework that explicitly reconstructs semantically meaningful sub-components from obfuscated compound expressions prior to Transformer encoding. CAEoT introduces a case factory that adaptively activates text- and URL-specific compound-aware decomposition modules, thereby exposing latent semantic cues before tokenization. The resulting enriched token sequences are then processed by an encoder-only Transformer and a lightweight classification head. Experiments conducted on two benchmark smishing datasets demonstrate that CAEoT achieves strong and consistent detection performance, with F1-scores of 0.89 and 0.98, ROC-AUC values of up to 1.00, and PR-AUC values of up to 0.99. Further analysis of ROC and precision–recall curves confirms that CAEoT maintains robust discriminative capability under class imbalance while preserving practical end-to-end inference efficiency. These results indicate that explicit compound-aware decomposition provides a principled and effective enhancement for Transformer-based smishing detection without modifying the underlying encoder architecture. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Supercomputing is the property of Springer Nature 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191658782 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A compound-aware encoder-only transformer model for smishing detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nguyen-Xuan%2C+Sam%22">Nguyen-Xuan, Sam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> samnx2@fe.edu.vn</i><br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Han%22">Nguyen, Han</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> han316@usf.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Mar2026, Vol. 82 Issue 4, p1-25. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+communication+system+security%22">Mobile communication system security</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Encoding%22">Encoding</searchLink><br /><searchLink fieldCode="DE" term="%22Phishing%22">Phishing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Smishing attacks increasingly exploit lexical obfuscation and compound-word constructions within both SMS content and embedded URLs, which significantly degrades the effectiveness of conventional tokenization-based detection approaches. To address this challenge, we propose the Compound-Aware Encoder-Only Transformer (CAEoT), a framework that explicitly reconstructs semantically meaningful sub-components from obfuscated compound expressions prior to Transformer encoding. CAEoT introduces a case factory that adaptively activates text- and URL-specific compound-aware decomposition modules, thereby exposing latent semantic cues before tokenization. The resulting enriched token sequences are then processed by an encoder-only Transformer and a lightweight classification head. Experiments conducted on two benchmark smishing datasets demonstrate that CAEoT achieves strong and consistent detection performance, with F1-scores of 0.89 and 0.98, ROC-AUC values of up to 1.00, and PR-AUC values of up to 0.99. Further analysis of ROC and precision–recall curves confirms that CAEoT maintains robust discriminative capability under class imbalance while preserving practical end-to-end inference efficiency. These results indicate that explicit compound-aware decomposition provides a principled and effective enhancement for Transformer-based smishing detection without modifying the underlying encoder architecture. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature 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.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11227-026-08321-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1 Subjects: – SubjectFull: Transformer models Type: general – SubjectFull: Mobile communication system security Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Text mining Type: general – SubjectFull: Encoding Type: general – SubjectFull: Phishing Type: general Titles: – TitleFull: A compound-aware encoder-only transformer model for smishing detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nguyen-Xuan, Sam – PersonEntity: Name: NameFull: Nguyen, Han IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09208542 Numbering: – Type: volume Value: 82 – Type: issue Value: 4 Titles: – TitleFull: Journal of Supercomputing Type: main |
| ResultId | 1 |
