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@article{183747,
        author = {Dr.V.Sridevi and Dr.SM Saravanakumar},
        title = {Social engineering and spam detection of AI-driven Phishing emails},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2725-2731},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183747},
        abstract = {Natural language processing has been transformed by the sophisticated design of advanced Language Models which produces text that accurately appears like authentic communication including phishing emails. Phishing emails created by AI are becoming more common these days. This investigation aims to address this problem by examining AI driven emails and address how well Email services filter these harmful messages. The results showed that that many email services allowed more AI-driven phishing emails to circumvent their filters.  The Generative AI social engineering conceptual model was incorporated to explore the complexity of Ai-driven social engineering attacks. In order to address these issues, logistic regression and XGBoost machine learning model were used to filter phishing emails based on factors the number of imperative verbs and personal pronouns. The Kaggle AI-generated phishing email dataset was used in this study.},
        keywords = {AI-driven phishing email, Textual and style analysis, Advanced Language Models (ALMs), Machine learning, cyber-attack},
        month = {August},
        }