Automatic Smishing Detection System with Feedback Loops

Automatic Smishing Detection System with Feedback Loops | 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 Automatic Smishing Detection System with Feedback Loops Guillaume Gallet, Guillaume Guerard, Sonia Djebali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4760693/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 This study delves into the escalating issue of Smishing, an emerging menace within the information security landscape spurred by the widespread use of text messages on smartphones. Smishing, a portmanteau of SMS and Phishing, involves attackers disseminating SMS with malicious content to unsuspecting victims. This content often harbors links that redirect users to websites housing malicious applications and deceptive user interfaces. The primary goal of this study is to construct an artificial intelligence-based Anti-Smishing filter capable of detecting and thwarting Smishing attempts. The societal significance of this research is underscored by the imperative to safeguard personal information in the face of a continuously evolving threat landscape. The detrimental impact of Smishing on individual security, encompassing both financial and personal realms, underscores the necessity for innovative solutions. Addressing this issue necessitates the development of a machine learning model specifically tailored to identify the distinct characteristics of Smishing. While the final results are pending, the strides made thus far indicate a substantial contribution toward the establishment of an effective filter. This research project is poised to yield tangible solutions to counteract Smishing, thereby fortifying the security of personal information in the global context of mobile communications. By creating a robust defense mechanism against Smishing attempts, this initiative aligns with the broader objective of enhancing cybersecurity and preserving the integrity of personal data in an increasingly interconnected and vulnerable digital environment. Cybersecurity Smishing SMS 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. 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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-4760693","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338658399,"identity":"088b2e0e-d676-48cf-9b0c-316a7b5b922f","order_by":0,"name":"Guillaume Gallet","email":"","orcid":"","institution":"Leonardo da Vinci Engineering School","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Gallet","suffix":""},{"id":338658400,"identity":"626daeb1-32e2-4e17-90a4-550411a32df6","order_by":1,"name":"Guillaume Guerard","email":"data:image/png;base64,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","orcid":"","institution":"Pôle Universitaire Léonard de Vinci","correspondingAuthor":true,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Guerard","suffix":""},{"id":338658401,"identity":"1ac0a40f-2ca7-4b9c-ac46-10877465fcb7","order_by":2,"name":"Sonia Djebali","email":"","orcid":"","institution":"Pôle Universitaire Léonard de Vinci","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Djebali","suffix":""}],"badges":[],"createdAt":"2024-07-18 07:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4760693/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4760693/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73166123,"identity":"82fe9725-8384-47b8-9f30-15cc11d72021","added_by":"auto","created_at":"2025-01-07 10:32:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1617809,"visible":true,"origin":"","legend":"","description":"","filename":"SNIJDSASmishing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4760693/v1_covered_c583a9c9-6bbc-49a6-a324-2fd880076de2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automatic Smishing Detection System with Feedback Loops","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, Smishing, SMS","lastPublishedDoi":"10.21203/rs.3.rs-4760693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4760693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study delves into the escalating issue of Smishing, an emerging menace within the information security landscape spurred by the widespread use of text messages on smartphones. Smishing, a portmanteau of SMS and Phishing, involves attackers disseminating SMS with malicious content to unsuspecting victims. This content often harbors links that redirect users to websites housing malicious applications and deceptive user interfaces. The primary goal of this study is to construct an artificial intelligence-based Anti-Smishing filter capable of detecting and thwarting Smishing attempts. The societal significance of this research is underscored by the imperative to safeguard personal information in the face of a continuously evolving threat landscape. The detrimental impact of Smishing on individual security, encompassing both financial and personal realms, underscores the necessity for innovative solutions. Addressing this issue necessitates the development of a machine learning model specifically tailored to identify the distinct characteristics of Smishing. While the final results are pending, the strides made thus far indicate a substantial contribution toward the establishment of an effective filter. This research project is poised to yield tangible solutions to counteract Smishing, thereby fortifying the security of personal information in the global context of mobile communications. 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