PhisNet: Deep learning-based Hybrid and Ensemble Multi-level Approach for the detection of phishing websites

PhisNet: Deep learning-based Hybrid and Ensemble Multi-level Approach for the detection of phishing websites | 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 PhisNet: Deep learning-based Hybrid and Ensemble Multi-level Approach for the detection of phishing websites Jayesh Soni, Nagarajan Prabakar, Himanshu Upadhyay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4283476/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 is a cyber-attack that intends to trick individuals into providing an attacker with sensitive information, such as login authorizations or business information. One tactic that attackers use is creating fake websites that mimic legitimate ones to fool victims into entering their information. A learning-based model can be used to analyze patterns in website content, structure, behavior, email text, sender, and links. These models can help identify phishing attempts and protect individuals and organizations from falling victim to these scams. In this research, we study and experiment with the deep learning-based algorithm in classifying phishing webpages from legitimate webpages that can be generalized across multiple domains. We used the open-source benchmark dataset, "Phishing Dataset for Machine Learning," available on Kaggle. We propose a hybrid and a multi-level ensemble approach for phishing website detection. Several one-class classifiers are trained on the first level, and the Variational Autoencoder (VAE) is used to reduce the size of feature vectors. Each one class classifiers have its own strength and limitations, and thus an adaptive weightage approach is applied. At the second level, a multilayer neural network is trained for classification. Further, the training time is reduced due to feature reduction in the latent space. Our experimental results classify phishing websites from legitimate websites with 98% accuracy. Artificial Intelligence and Machine Learning Phishing Website Detection Variational AutoEncoder One Class Classifiers Neural Network Full Text Additional Declarations The authors declare no competing interests. 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. 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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-4283476","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292463727,"identity":"890f5f95-eb11-426e-8f9c-faa3d27e56f3","order_by":0,"name":"Jayesh Soni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYBACxgYgkVAhxwPiSJCg5YwxCVog+tqMGYjXwtx/+NiDh/MMZAwOMB+8zUOUBTPS0g0StxnwGBxgS7YmUguPmUTitj9ALTxm0sRp6T8D1DIHZAv/NyK1NOQAtTSAtPCwEallRlqaRMIxAx7Jw2zGlnOI0WIIDDHJHzUG9nzHmx/eeEOUlgYYi5kY5SAgT6zCUTAKRsEoGMEAAIpAKyUOqJpbAAAAAElFTkSuQmCC","orcid":"","institution":"Florida International University","correspondingAuthor":true,"prefix":"","firstName":"Jayesh","middleName":"","lastName":"Soni","suffix":""},{"id":292464736,"identity":"ae4926ea-bfcb-4b78-b97e-f509c32baf7f","order_by":1,"name":"Nagarajan Prabakar","email":"","orcid":"","institution":"Florida International University","correspondingAuthor":false,"prefix":"","firstName":"Nagarajan","middleName":"","lastName":"Prabakar","suffix":""},{"id":292464805,"identity":"5637ca21-ecd5-4f73-b9b8-39732158c0d7","order_by":2,"name":"Himanshu Upadhyay","email":"","orcid":"","institution":"Florida International University","correspondingAuthor":false,"prefix":"","firstName":"Himanshu","middleName":"","lastName":"Upadhyay","suffix":""}],"badges":[],"createdAt":"2024-04-17 17:54:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4283476/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4283476/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54874428,"identity":"4a29cab7-a282-41dd-86eb-0713f449c1ab","added_by":"auto","created_at":"2024-04-18 02:33:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":561995,"visible":true,"origin":"","legend":"","description":"","filename":"Jsoniv3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4283476/v1_covered_df1d3c1d-e8f5-4dff-bbce-f374e2991820.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePhisNet: Deep learning-based Hybrid and Ensemble Multi-level Approach for the detection of phishing websites\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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 Website Detection, Variational AutoEncoder, One Class Classifiers, Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-4283476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4283476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhishing is a cyber-attack that intends to trick individuals into providing an attacker with sensitive information, such as login authorizations or business information. One tactic that attackers use is creating fake websites that mimic legitimate ones to fool victims into entering their information. A learning-based model can be used to analyze patterns in website content, structure, behavior, email text, sender, and links. These models can help identify phishing attempts and protect individuals and organizations from falling victim to these scams. In this research, we study and experiment with the deep learning-based algorithm in classifying phishing webpages from legitimate webpages that can be generalized across multiple domains. We used the open-source benchmark dataset, \"Phishing Dataset for Machine Learning,\" available on Kaggle. We propose a hybrid and a multi-level ensemble approach for phishing website detection. Several one-class classifiers are trained on the first level, and the Variational Autoencoder (VAE) is used to reduce the size of feature vectors. 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