• Journal of the Semiconductor & Display Technology
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• v.23 no.3
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• pp.115-120
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• 2024

As smartphones become more integral to daily life, security concerns, particularly regarding smishing, have risen significantly. With the increasing frequency and variety of smishing attacks, current detection methods struggle to provide effective solutions, with most commercial algorithms achieving around a 70% detection rate. This paper proposes a novel approach to enhancing smishing detection accuracy by utilizing Natural Language Processing to analyze message syntax and semantics, combined with URL Punycode conversion and whitelist techniques. The approach focuses on improving detection through comprehensive message analysis, aiming to address the limitations of existing preventive methods. The proposed system offers conceptual improvements in smishing detection strategies, providing a more robust framework for addressing evolving security challenges.