A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment

A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment | 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 Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment Brij B. Gupta, Akshat Gaurav, Razaz Waheeb Attar, Varsha Arya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4022443/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 There is a serious risk to information privacy and security in the IoT ecosystem from phishing attacks. This research offers a unique strategy for increasing phishing attack detection by employing the Cuckoo Search algorithm to tune hyperparameters in a deep Convolutional Neural Network (CNN) model. Our suggested approach is very effective, with a 90% success rate in detecting phishing attacks. To distinguish between legal and fraudulent IoT network activity, we systematically tune hyperparameters using Cuckoo Search to optimise the deep CNN model's performance. This study helps ensure the safety of sensitive information and IoT devices by providing a novel and effective approach for real-time phishing detection. Deep Learning Phishing Cuckoo Search CNN IoT 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-4022443","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277597699,"identity":"5b7630bd-2bae-48ac-9497-71f7ffc6264a","order_by":0,"name":"Brij B. Gupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYPCCAwxsDIcPABkSMsQoZ2yAaDmWANLCQ7wWBgYeAxCPsBb5iNzjD34w3MnnYzzz+dWNGgseBvbDRzfg02J4Iy+xsYfhmWUbw9lt1jnHgA7jSUu7gVfLjBzDBh6GwwZsQC3GOWxALRI8ZgS1NP4BaznzzDjnHxFa5CVyDJshtpxhfpzbRoQWA553ibNlDJ4BtRwzY87tk+BhI+QX+fbcAx/fVNwxkJ9x+PHnnG91cvzsh4/ht+UAKCJAMSJxgE0CJMKGTznYlgZY3PE3MH8gpHoUjIJRMApGJgAAWf5JNF8vkiQAAAAASUVORK5CYII=","orcid":"","institution":"ASIA UNIVERSITY","correspondingAuthor":true,"prefix":"","firstName":"Brij","middleName":"B.","lastName":"Gupta","suffix":""},{"id":277597700,"identity":"543c7eb2-88b8-4cb2-bb59-0266bbe128b4","order_by":1,"name":"Akshat Gaurav","email":"","orcid":"","institution":"Ronin Institute","correspondingAuthor":false,"prefix":"","firstName":"Akshat","middleName":"","lastName":"Gaurav","suffix":""},{"id":277597701,"identity":"b3b0f025-2c0c-4150-bade-05fb7a231d0f","order_by":2,"name":"Razaz Waheeb Attar","email":"","orcid":"","institution":"Princess Nourah bint Abdulrahman University","correspondingAuthor":false,"prefix":"","firstName":"Razaz","middleName":"Waheeb","lastName":"Attar","suffix":""},{"id":277597704,"identity":"a7a928b4-767b-4787-88c3-87c8f6ccc63c","order_by":3,"name":"Varsha Arya","email":"","orcid":"","institution":"ASIA UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"Varsha","middleName":"","lastName":"Arya","suffix":""}],"badges":[],"createdAt":"2024-03-07 00:48:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4022443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4022443/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53367218,"identity":"f204f058-f226-4bc9-ba62-179f6b750710","added_by":"auto","created_at":"2024-03-25 06:54:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":830948,"visible":true,"origin":"","legend":"","description":"","filename":"ANovelCuckooSearchBasedOptimizedDeepCNNModelforPhishingAttackDetectioninIoTEnvironmentfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022443/v1_covered_745ec6de-45b3-419c-b037-a9195f0ea541.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment","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":"Deep Learning, Phishing, Cuckoo Search, CNN, IoT","lastPublishedDoi":"10.21203/rs.3.rs-4022443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4022443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"There is a serious risk to information privacy and security in the IoT ecosystem from phishing attacks. This research offers a unique strategy for increasing phishing attack detection by employing the Cuckoo Search algorithm to tune hyperparameters in a deep Convolutional Neural Network (CNN) model. Our suggested approach is very effective, with a 90\\% success rate in detecting phishing attacks. To distinguish between legal and fraudulent IoT network activity, we systematically tune hyperparameters using Cuckoo Search to optimise the deep CNN model's performance. This study helps ensure the safety of sensitive information and IoT devices by providing a novel and effective approach for real-time phishing detection.","manuscriptTitle":"A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 17:48:31","doi":"10.21203/rs.3.rs-4022443/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"f77a8391-89ef-4724-8584-ce79cd18fb91","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-25T06:45:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 17:48:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4022443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4022443","identity":"rs-4022443","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}