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@article{173131,
        author = {Indhumathi S and Santhosh Adaikalaraj S and Abinash V and Reshma K},
        title = {Cyber Protector: Advanced Machine Learning  Algorithm for Phishing Website Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {1929-1931},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173131},
        abstract = {Phishing attacks are one of the most pervasive cyber threats in today’s digital era, targeting individuals and organizations by mimicking legitimate entities to steal sensitive data. As phishing tactics grow more sophisticated, traditional detection methods struggle to keep pace. Cyber Protector addresses this challenge by employing machine learning techniques, specifically Gradient Boosting and XGBoost algorithms, to monitor and detect phishing websites effectively. This solution offers superior detection accuracy, reduced false positives, and adaptability, ensuring a secure browsing experience. By integrating real-time monitoring and continuous learning, Cyber Protector presents a scalable approach to combating evolving phishing threats.},
        keywords = {},
        month = {February},
        }