A Secure Approach to Detect Phishing Emails Based on Machine Learning and Deep Learning

A Secure Approach to Detect Phishing Emails Based on Machine Learning and Deep Learning | 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 Secure Approach to Detect Phishing Emails Based on Machine Learning and Deep Learning Mohamed Khayati, Driss Ait Omar, Mohamed Baslam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7002495/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 Detecting phishing emails remains a real challenge in cybersecurity, especially as attackers are constantly finding new ways to bypass traditional defence systems. This study provides an in-depth comparison between traditional machine learning algorithms (such as Naive Bayes, Logistic Regression, SGDClassifier, XGBoost, Decision Tree, Random Forest and MLPClassifier) and more advanced deep learning models (such as LSTM, BiLSTM and GRU) in the context of phishing attack detection. We tested these models on a dataset of emails, using features extracted from both the headers and the content of the messages. The machine learning algorithms showed impressive results, with accuracies ranging from 96.01% to 98.77%. More specifically, the results were as follows: 97.92%, 98.41%, 98.77%, 97.57%, 96.01%, 98.34% and 98.77%. But on the deep learning side, performance was even better, reaching accuracies of 99.8%, 99.9% and 99.8%. This highlights the ability of these models to detect more complex attacks, using increasingly sophisticated social engineering techniques. These results underline the full potential of deep learning models for developing powerful and f lexible phishing detection systems, capable of adapting to the challenges of real-life applications. Cybersecurity SGDClassifier XGBoost Decision Tree Random Forest MLPClassifier LSTM BiLSTM GRU 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. 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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-7002495","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495972600,"identity":"efc503b5-3c45-419d-9c3a-d83851ae148b","order_by":0,"name":"Mohamed Khayati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYFACHhDBzCAhAeZJyJGqJUHCmGQtDIkNhDTotvce/HSjwppBcnbzM4mfPyzSN5w/nfyBoYYhmh+HZrMz55Klc86kM0jLHDOT7EmQyN1wI3ebBMMxhtwZB3BouZFjIJ3bdphBTiLBTIIHrIV3GwMDG0NuA24txr9z/4G0pH+T/JMgkW5w/uzmDwz/GHLn49ZiJp3bcJhBWgLIANqSYHAgd4MEYxtD7gZcWs6cMbPOOZbOIzkjp9haJk3CcCbIL4l9ErkbcWk53mN8O6fGWk7iRvrGm29s6uT5QA778M0mdx4OLTAAih0WCTg3gUECt1okwPyBKGWjYBSMglEw4gAA1TFcMZJ2Vw8AAAAASUVORK5CYII=","orcid":"","institution":"Université Sultan Moulay Slimane","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Khayati","suffix":""},{"id":495972602,"identity":"e23893b3-812c-4f44-9bd8-02a787f6a41c","order_by":1,"name":"Driss Ait Omar","email":"","orcid":"","institution":"Université Sultan Moulay Slimane","correspondingAuthor":false,"prefix":"","firstName":"Driss","middleName":"Ait","lastName":"Omar","suffix":""},{"id":495972604,"identity":"2df78b76-3d8e-41d1-9d16-d064654acd31","order_by":2,"name":"Mohamed Baslam","email":"","orcid":"","institution":"Université Sultan Moulay Slimane","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Baslam","suffix":""}],"badges":[],"createdAt":"2025-06-29 11:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7002495/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7002495/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104536313,"identity":"32f72fc4-86a7-4c51-a70d-daad95e3d32e","added_by":"auto","created_at":"2026-03-13 03:55:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":964734,"visible":true,"origin":"","legend":"","description":"","filename":"ASecureApproachtoDetectPhishingEmailsBasedonMachineLearningandDeepLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7002495/v1_covered_af5c689a-4fde-4835-ac63-4663568a97e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Secure Approach to Detect Phishing Emails Based on Machine Learning and Deep Learning","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":"Cybersecurity, SGDClassifier, XGBoost, Decision Tree, Random Forest, MLPClassifier, LSTM, BiLSTM, GRU","lastPublishedDoi":"10.21203/rs.3.rs-7002495/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7002495/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":" Detecting phishing emails remains a real challenge in cybersecurity, especially as attackers are constantly finding new ways to bypass traditional defence systems. 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