An Enhanced Machine Learning with NLP Modelling Technique for Smishing Attacks Detection in Low-Resourced Languages

An Enhanced Machine Learning with NLP Modelling Technique for Smishing Attacks Detection in Low-Resourced Languages | 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 An Enhanced Machine Learning with NLP Modelling Technique for Smishing Attacks Detection in Low-Resourced Languages Aaron Zimba, Katongo Ongani Phiri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7521286/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 Smishing, a form of phishing through SMS, has emerged as a significant cybersecurity threat, particularly on mobile money platforms in regions with limited cybersecurity awareness. This research introduces a robust machine learning model integrated with advanced natural language processing (NLP) techniques for effective smishing detection. The proposed model targets English and Bemba, a low-resourced language, addressing a critical gap in cybersecurity research for linguistically diverse, resource-constrained environments. The model incorporates pseudonymization to enhance data security by anonymizing sensitive information such as personal identifiers while retaining the contextual integrity of messages. Named Entity Recognition (NER) is employed to detect and mask sensitive entities, further safeguarding user privacy. To bolster model robustness against adversarial attacks, adversarial training is applied, exposing the model to perturbed inputs during training to improve its resilience to manipulation. Regularization techniques, specifically L1 regularization, are used to optimize the model by reducing overfitting and ensuring efficient performance. The evaluation utilized datasets in English, Bemba, and a combination of both to assess the model’s adaptability to multilingual inputs. The results demonstrate superior performance, with high F1-Scores, low log loss, and across datasets, AUC ranged from 0.93 (Bemba) to 0.98 (English–Bemba), with consistently strong F1 and MCC. These metrics underscore the model’s capability to distinguish between smishing and legitimate messages effectively. By combining machine learning and NLP in a privacy-preserving and security-enhanced framework, this research provides a scalable, efficient solution for smishing detection in under-resourced contexts, contributing significantly to advancements in cybersecurity for low-resourced languages. pseudonymization low-resourced language adversarial training mobile money platforms data privacy 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-7521286","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524380519,"identity":"8639ee6d-8a38-4c78-a95e-8db2f281b239","order_by":0,"name":"Aaron 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This research introduces a robust machine learning model integrated with advanced natural language processing (NLP) techniques for effective smishing detection. The proposed model targets English and Bemba, a low-resourced language, addressing a critical gap in cybersecurity research for linguistically diverse, resource-constrained environments. The model incorporates pseudonymization to enhance data security by anonymizing sensitive information such as personal identifiers while retaining the contextual integrity of messages. Named Entity Recognition (NER) is employed to detect and mask sensitive entities, further safeguarding user privacy. To bolster model robustness against adversarial attacks, adversarial training is applied, exposing the model to perturbed inputs during training to improve its resilience to manipulation. Regularization techniques, specifically L1 regularization, are used to optimize the model by reducing overfitting and ensuring efficient performance. The evaluation utilized datasets in English, Bemba, and a combination of both to assess the model\u0026rsquo;s adaptability to multilingual inputs. The results demonstrate superior performance, with high F1-Scores, low log loss, and across datasets, AUC ranged from 0.93 (Bemba) to 0.98 (English\u0026ndash;Bemba), with consistently strong F1 and MCC. These metrics underscore the model\u0026rsquo;s capability to distinguish between smishing and legitimate messages effectively. By combining machine learning and NLP in a privacy-preserving and security-enhanced framework, this research provides a scalable, efficient solution for smishing detection in under-resourced contexts, contributing significantly to advancements in cybersecurity for low-resourced languages.\u003c/p\u003e","manuscriptTitle":"An Enhanced Machine Learning with NLP Modelling Technique for Smishing Attacks Detection in Low-Resourced Languages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 18:10:32","doi":"10.21203/rs.3.rs-7521286/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":"c15f7415-5662-4051-b2b6-c1273643b434","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-09T12:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 18:10:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7521286","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7521286","identity":"rs-7521286","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}