A Noise-Resilient Attention Guided Contrastive Learning Framework for Phishing Email Detection.
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| Title: | A Noise-Resilient Attention Guided Contrastive Learning Framework for Phishing Email Detection. |
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| Authors: | Gueddoudj, El Yazid1,2, Attia, Abdelouahab3,4, Chikh, Azeddine2,5 elyazid.gueddoudj@univ-batna.dz |
| Source: | International Journal of Performability Engineering. Jun2026, Vol. 22 Issue 6, p297-308. 12p. |
| Subjects: | Phishing, Contrastive learning, Email security, Internet security, Adversarial machine learning |
| Abstract: | Phishing emails are a serious cybersecurity risk because they take advantage of people's weaknesses to obtain private information. Both conventional heuristics and classical machine learning techniques are undermined by their intrinsically noisy, obfuscated, and constantly changing character. Because they are unable to learn invariant, discriminative representations and deliberately suppress noise, even the most advanced deep learning models have limited robustness. In this paper, we provide a novel noise-augmented, attention-guided contrastive learning framework for reliable phishing email detection in order to overcome these difficulties. However, we introduce two crucial novelties: (i) the noise augmentation policy, producing perturbed views of an email to account for variability and obfuscation in the wild, and (ii) an attention-guided contrastive learning method to focus on more informative features and discard noisy ones in contrastive representation learning. By leveraging this synergy, the model can obtain class-discriminative and noise-invariant embeddings, guaranteeing accurate identification even in the face of extreme adversarial perturbations. Comprehensive evaluations on the publicly accessible Phishing Email Detection dataset show that the proposed framework routinely achieves accuracy gains of at least 1.5% and 1.22%, respectively, over twelve cutting-edge baselines. These findings confirm the efficacy and superiority of the suggested method for acquiring reliable, noise-resistant representations for precise phishing detection in practical settings. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194978492 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Noise-Resilient Attention Guided Contrastive Learning Framework for Phishing Email Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gueddoudj%2C+El+Yazid%22">Gueddoudj, El Yazid</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Attia%2C+Abdelouahab%22">Attia, Abdelouahab</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Chikh%2C+Azeddine%22">Chikh, Azeddine</searchLink><relatesTo>2,5</relatesTo><i> elyazid.gueddoudj@univ-batna.dz</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. Jun2026, Vol. 22 Issue 6, p297-308. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Phishing%22">Phishing</searchLink><br /><searchLink fieldCode="DE" term="%22Contrastive+learning%22">Contrastive learning</searchLink><br /><searchLink fieldCode="DE" term="%22Email+security%22">Email security</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Adversarial+machine+learning%22">Adversarial machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Phishing emails are a serious cybersecurity risk because they take advantage of people's weaknesses to obtain private information. Both conventional heuristics and classical machine learning techniques are undermined by their intrinsically noisy, obfuscated, and constantly changing character. Because they are unable to learn invariant, discriminative representations and deliberately suppress noise, even the most advanced deep learning models have limited robustness. In this paper, we provide a novel noise-augmented, attention-guided contrastive learning framework for reliable phishing email detection in order to overcome these difficulties. However, we introduce two crucial novelties: (i) the noise augmentation policy, producing perturbed views of an email to account for variability and obfuscation in the wild, and (ii) an attention-guided contrastive learning method to focus on more informative features and discard noisy ones in contrastive representation learning. By leveraging this synergy, the model can obtain class-discriminative and noise-invariant embeddings, guaranteeing accurate identification even in the face of extreme adversarial perturbations. Comprehensive evaluations on the publicly accessible Phishing Email Detection dataset show that the proposed framework routinely achieves accuracy gains of at least 1.5% and 1.22%, respectively, over twelve cutting-edge baselines. These findings confirm the efficacy and superiority of the suggested method for acquiring reliable, noise-resistant representations for precise phishing detection in practical settings. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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: 10.23940/ijpe.26.06.p1.297308 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 297 Subjects: – SubjectFull: Phishing Type: general – SubjectFull: Contrastive learning Type: general – SubjectFull: Email security Type: general – SubjectFull: Internet security Type: general – SubjectFull: Adversarial machine learning Type: general Titles: – TitleFull: A Noise-Resilient Attention Guided Contrastive Learning Framework for Phishing Email Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gueddoudj, El Yazid – PersonEntity: Name: NameFull: Attia, Abdelouahab – PersonEntity: Name: NameFull: Chikh, Azeddine IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09731318 Numbering: – Type: volume Value: 22 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Performability Engineering Type: main |
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