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@article{176411,
        author = {Prajwal H and Chirag K and Deepak reddy R and Lochan S},
        title = {Malicious Webpage Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {8090-8095},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176411},
        abstract = {As the Internet experiences exponential growth, the risks associated with cybercrime have escalated, particularly through phishing attacks and deceptive online tactics. Cybercriminals create seemingly legitimate websites to manipulate users to reveal sensitive information such as login credentials, financial details, and personal data. This study focuses on developing a machine learning-based system for detecting and classifying malicious websites with the goal of preventing data from being phished. By evaluating multiple machine-learning models and leveraging feature engineering, we identified the most effective approach for accurate classification. A hugging-face model, which demonstrated the highest accuracy, was selected and integrated into a browser extension for real-time web protection. The proposed methodology enhances web security by preventing users from accessing harmful websites, thereby offering a practical and scalable solution for mitigating cyber threats.},
        keywords = {Internet, cybercrime, phishing attacks, deceptive tactics, malicious websites, machine learning, feature engineering, classification, hugging-face model, browser extension, web security, cyber threats, real-time protection, data privacy.},
        month = {May},
        }