A Novel GRU-Attention Framework with Adaptive Authentication for Robust Phishing Attack Detection and Secure Data Transfer

A Novel GRU-Attention Framework with Adaptive Authentication for Robust Phishing Attack Detection and Secure Data Transfer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel GRU-Attention Framework with Adaptive Authentication for Robust Phishing Attack Detection and Secure Data Transfer Qaisar Abbas, Mubarak Albathan, Imran Qureshi, Mutlaq B. Aldajani, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7946125/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Phishing attacks have become increasingly sophisticated, using advanced techniques to mimic legitimate websites and trick users into revealing sensitive information. Traditional phishing detection models often fail to cope with the evolving nature of these attacks, particularly because they struggle to capture the sequential patterns inherent in phishing transactions. To address this limitation, we propose an enhanced phishing detection framework based on a gated recurrent unit (GRU) with an attention mechanism, designed to achieve more robust and accurate detection. The system integrates adaptive authentication, which applies dynamic security measures to ensure safe data transfer. Unlike previous approaches that focused solely on detection accuracy, our framework introduces a novel integration of GRU + Attention with adaptive authentication, enabling both accurate detection and context-aware protection. Data collection is carried out using the credible OpenPhish source. Preprocessing involves Min-Max normalization, followed by feature extraction with artificial neural networks (ANN), while the Fox optimizer is applied for feature selection to balance exploration and exploitation. Experimental evaluation demonstrates that the proposed model achieves an accuracy of 99.19%, with precision of 99.11% and recall of 99.88%. Compared with existing approaches such as random forest, ensemble learning, and FastText with LSTM, the GRU with attention mechanism consistently outperforms across all metrics. This approach offers a flexible and scalable solution for mitigating phishing threats in dynamic online environments. Phishing attack detection GRU Attention mechanism Adaptive authentication Cybersecurity in AI Deep learning Secure data transfer AI-driven threat mitigation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7946125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541859821,"identity":"36e2634e-15a1-4570-90b9-da1aa4997dfa","order_by":0,"name":"Qaisar Abbas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYHACNmYgIccG5TE2EKvFmHQtiTCVhLXwHT/87HFBTV16n9jhp5t5GGxkNxxgf/gBnxbJM2nmxjOOHc5tk04zu83DkGa84QCPsQQ+LQYHEsykedgOALUkgLQcTgRqYcCv5fzzb9I8/+rS2aTTvwG1/AdqYX/8A6+WGzlm0rxtzAls0jkgWw4AtTCY4bVF8sabcuOZfYcN26Rzym7OMUg2nnmYx8wCnxa+8+nbHhd8q5OXn52+7cabCjvZvuPtj2/g08JwANWdQMyMVz2GllEwCkbBKBgFWAAAXiBLRzYOh0sAAAAASUVORK5CYII=","orcid":"","institution":"Imam Mohammad Ibn Saud Islamic University (IMSIU)","correspondingAuthor":true,"prefix":"","firstName":"Qaisar","middleName":"","lastName":"Abbas","suffix":""},{"id":541859822,"identity":"05596741-0af3-433f-a533-a3fd212aa64f","order_by":1,"name":"Mubarak Albathan","email":"","orcid":"","institution":"Imam Mohammad Ibn Saud Islamic University (IMSIU)","correspondingAuthor":false,"prefix":"","firstName":"Mubarak","middleName":"","lastName":"Albathan","suffix":""},{"id":541859823,"identity":"711a6409-0421-4e37-aeae-1bec8af3276c","order_by":2,"name":"Imran Qureshi","email":"","orcid":"","institution":"Imam Mohammad Ibn Saud Islamic University (IMSIU)","correspondingAuthor":false,"prefix":"","firstName":"Imran","middleName":"","lastName":"Qureshi","suffix":""},{"id":541859824,"identity":"b98a5c0d-40e2-4b09-83ac-2f5646efac20","order_by":3,"name":"Mutlaq B. 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Transfer","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Phishing attack detection, GRU, Attention mechanism, Adaptive authentication, Cybersecurity in AI, Deep learning, Secure data transfer, AI-driven threat mitigation","lastPublishedDoi":"10.21203/rs.3.rs-7946125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7946125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhishing attacks have become increasingly sophisticated, using advanced techniques to mimic legitimate websites and trick users into revealing sensitive information. Traditional phishing detection models often fail to cope with the evolving nature of these attacks, particularly because they struggle to capture the sequential patterns inherent in phishing transactions. To address this limitation, we propose an enhanced phishing detection framework based on a gated recurrent unit (GRU) with an attention mechanism, designed to achieve more robust and accurate detection. The system integrates adaptive authentication, which applies dynamic security measures to ensure safe data transfer. Unlike previous approaches that focused solely on detection accuracy, our framework introduces a novel integration of GRU\u0026thinsp;+\u0026thinsp;Attention with adaptive authentication, enabling both accurate detection and context-aware protection. Data collection is carried out using the credible OpenPhish source. Preprocessing involves Min-Max normalization, followed by feature extraction with artificial neural networks (ANN), while the Fox optimizer is applied for feature selection to balance exploration and exploitation. Experimental evaluation demonstrates that the proposed model achieves an accuracy of 99.19%, with precision of 99.11% and recall of 99.88%. Compared with existing approaches such as random forest, ensemble learning, and FastText with LSTM, the GRU with attention mechanism consistently outperforms across all metrics. 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