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Machine Learning Driven Smishing Detection Framework for Mobile Security

arxiv.org · 7,564 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. 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 Naïve Bayesian classifier, in distin

Machine Learning Driven Smishing Detection Framework for Mobile Security Diksha Goel 1 , 2 ∗ 1 superscript 2 {}^{1,2^{*}} start_FLOATSUPERSCRIPT 1 , 2 start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT end_FLOATSUPERSCRIPT , Hussain Ahmad 3 , Ankit Kumar Jain 2 , Nikhil Kumar Goel 4 Email: diksha.goel@data61.csiro.au; hussain.ahmad@adelaide.edu.au; ankitjain@nitkkr.ac.in; nikhilgoel.kkr@gmail.com Corresponding author 1 CSIRO’s Data61, Australia 2 National Institute of Technology, Kurukshetra, India 3 University of Adelaide, Australia 4 PGIMS, Haryana, India Abstract The increasing reliance on smartph

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