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@article{178161,
        author = {Mr.Matcha Deepak and Ms.Elampirai Gopika},
        title = {Adversarial Smishing Attacks: Detecting and Defending Against Evolving Evasion Techniques in Mobile Money Fraud},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3249-3253},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178161},
        abstract = {Smishing, or SMS phishing, has emerged as a prevalent cyber threat that targets mobile money transactions. Attackers leverage adversarial evasion techniques to bypass traditional detection methods, thereby increasing the complexity of the defense mechanisms. This study investigates the evolving nature of adversarial smishing attacks and presents a robust machine-learning-based approach to detect and mitigate them. Our proposed methodology involves data collection, feature extraction, and the evaluation of multiple classification algorithms. The results demonstrate that deep learning models outperform traditional machine learning techniques in identifying fraudulent SMS messages. This study aims to provide a comprehensive understanding of smishing attacks, while offering an effective defense strategy for mobile money users.},
        keywords = {AI, NLP, Malware Detection, Smishing, Mobile Money, Adversarial Attacks},
        month = {May},
        }