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@article{201987,
        author = {Disha Badgujar and Yashika Thakur and Anjali Tiwari and Meghansh Saxena and Rajdeep Shrivastava},
        title = {Machine Learning Based Phishing URL Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {5628-5630},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201987},
        abstract = {Phishing attacks have emerged as one of the most dangerous cybersecurity threats in the digital era. Attackers use deceptive websites and malicious URLs to steal confidential information such as usernames, passwords, and banking credentials. Traditional phishing detection systems based on blacklists and heuristic methods are often unable to identify newly generated phishing websites. This research paper presents a machine learning based phishing URL detection system capable of classifying URLs as legitimate or phishing using extracted lexical and domain-based features. Various machine learning algorithms including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Gradient Boosting were trained and evaluated. Among all the models, Gradient Boosting achieved the highest accuracy of approximately 97%. The trained model was integrated into a Flask-based web application to provide real-time phishing detection. The proposed system offers a scalable, efficient, and intelligent approach for improving cybersecurity and protecting users from online threats.},
        keywords = {},
        month = {May},
        }