[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122565-en":3,"doc-seo-122565-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122565,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning Driven Smishing Detection Framework for Mobile Security - Enhanced Text Normalization","Smartphones’ dominance in communication, financial activities, and personal data handling makes them attractive targets for cyberattacks, especially smishing—phishing delivered through SMS. Conventional approaches often underperform because SMS language is informal and constantly evolving, containing abbreviations, slang, and short forms. This work introduces a content-based smishing detection framework that applies advanced text normalization to convert non-standard SMS into a standardized representation. The normalized content improves machine learning classifiers, notably the Naive Bayesian model, enabling more reliable separation of smishing from legitimate messages. Experiments on a public dataset achieve 96.2% detection accuracy with low false positive and false negative rates (3.87% and 2.85%), outperforming existing methods for mobile security.","Machine Learning Driven Smishing Detection Framework for Mobile Security  \nDiksha Goel 1 ,2 ∗ , Hussain Ahmad3 , Ankit Kumar Jain2 , Nikhil Kumar Goel4  \n1 CSIRO’s Data61, Australia  \n2National Institute of Technology, Kurukshetra, India  \n3University of Adelaide, Australia  \n4PGIMS, Haryana, India  \nEmail: [diksha.goel@data61.csiro.au](diksha.goel@data61.csiro.au) ; [hussain.ahmad@adelaide.edu.au](hussain.ahmad@adelaide.edu.au) ; [ankitjain@nitkkr.ac.in](ankitjain@nitkkr.ac.in) ; [nikhilgoel.kkr@gmail.com](nikhilgoel.kkr@gmail.com)  \narXiv :2412 .0964 1v 1 [ cs .CR] 9 Dec 2024  \nAbstract—The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the informal and evolving nature of SMS language, which includes abbreviations, slang, and short forms. This paper presents an enhanced content-based smishing detection framework that leverages advanced text normalization techniques to improve detection accuracy. By converting non-standard text into its standardized form, the proposed model enhances the efficacy of machine learning classifiers, particularly the Na Bayesian classifier, in distinguishing smishing messages from legitimate ones. Our experimental results, validated on a publicly available dataset, demonstrate a detection accuracy of 96.2%, with a low False Positive Rate of 3.87% and False Negative Rate of 2.85% . This approach significantly outperforms existing methodologies, providing a robust solution to the increasingly sophisticated threat of smishing in the mobile environment.  \nIndex Terms—Cybersecurity, Smishing, Mobile Security, Machine Learning, Short Message Services, Smartphones.  \nI. INTRODUCTION  \nThe rapid advancement of technology has dramatically transformed our operational frameworks, mainly through the proliferation of smart devices such as smartphones and tablets [4],[28] . Among these, smartphones have become an essential part of our daily lives, offering unparalleled convenience and functionality [5], [6] . With over 7.211 billion users worldwide and 23 billion texts messages sent daily, smartphones are now indispensable tools for communication, business, and personal tasks. This widespread adoption, however, has come with its drawbacks. The increasing reliance on smartphones has made them prime targets for cybercriminals, leading to a significant rise in cybercrime incidents [1], [39] .  \nAs the integration of smartphones into our lives deepens, so does the sophistication of cyber threats targeting these devices [21] . Cyber threats targeting smartphones have evolved in both sophistication and frequency. These threats include viruses, malware, Denial of Service (DoS) attacks [27],[30], and social engineering tactics like phishing attacks [31] . Among these, phishing has emerged as one of the most pervasive and damaging forms of cyberattack [4], where attackers impersonate  \n* Corresponding Author  \nlegitimate entities to deceive users into divulging sensitive information such as credit card numbers, bank account details, and personal data [5] . These attacks often result in substantial financial losses for both individuals and organizations [7] .  \nBuilding upon the persistent nature of phishing, this threat has evolved significantly, particularly impacting mobile devices [34] . Since the term was first coined in 1996, phishing has continuously adapted to exploit new technological vulnerabilities [32]. In recent years, phishing attacks have increasingly targeted mobile devices, leveraging the ubiquitous presence of smartphones [21], [18] . Attackers now commonly employ Trojans, viruses, and ransomware in conjunction with phishing techniques to compromise smartphone security [36], [9] .  \nThe immediacy and personal nature of mobile communication make sm","cbCaiviGyaIIit8l","https://ap.wps.com/l/cbCaiviGyaIIit8l","pdf",696932,1,10,"English","en",105,"# Introduction\n## Smishing and phishing background\n## SMS effectiveness and user awareness\n## Real-world impact\n# Proposed Framework\n## Content-based detection using text normalization\n## Machine learning classification approach\n# Experiments and Results\n## Dataset validation\n## Accuracy and error-rate evaluation\n# Conclusion","[{\"question\":\"Why do traditional smishing detection methods struggle with SMS messages?\",\"answer\":\"SMS text is informal and continuously changes, often containing abbreviations, slang, and short forms. This makes patterns learned from standard text less effective for detecting smishing reliably.\"},{\"question\":\"What does the proposed framework do differently?\",\"answer\":\"It uses advanced text normalization to convert non-standard SMS into a standardized form, then applies machine learning classifiers to distinguish smishing from legitimate messages.\"},{\"question\":\"How effective is the framework based on the reported experiments?\",\"answer\":\"On a publicly available dataset, it reaches 96.2% detection accuracy, with false positive rate of 3.87% and false negative rate of 2.85%.\"}]","Machine Learning Driven Smishing Detection Framework for Mobile Security - Enhanced Text Normalization | PDF",1785811343,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-driven-smishing-detection-framework-for-mobile-security-enhanced-text-normalization","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-driven-smishing-detection-framework-for-mobile-security-enhanced-text-normalization/122565/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do traditional smishing detection methods struggle with SMS messages?","Question",{"text":75,"@type":76},"SMS text is informal and continuously changes, often containing abbreviations, slang, and short forms. This makes patterns learned from standard text less effective for detecting smishing reliably.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed framework do differently?",{"text":80,"@type":76},"It uses advanced text normalization to convert non-standard SMS into a standardized form, then applies machine learning classifiers to distinguish smishing from legitimate messages.",{"name":82,"@type":73,"acceptedAnswer":83},"How effective is the framework based on the reported experiments?",{"text":84,"@type":76},"On a publicly available dataset, it reaches 96.2% detection accuracy, with false positive rate of 3.87% and false negative rate of 2.85%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]