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@article{185803,
        author = {Suyog Vilas Patil and Dr. Vijay Pal Singh},
        title = {An Enhanced Detection Model for Phishing Websites Using ABAC, Heuristic Techniques, and Canopy Feature Optimization},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {8-14},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185803},
        abstract = {Phishing is a broad tactic used by scammed people to reveal their personal data using false websites. The URL of a phishing website is designed to steal personal data such as usernames, passwords, and online finance activities. Phisher uses aesthetically and linguistically similar websites for these real websites. A powerful tool to disrupt phishing attacks is machine learning. Intruders often use phishing as it is easier to deceive the victim by clicking on malicious links that look real than trying to overcome computer security measures. The presented method uses machine learning to create innovative approaches to recognizing phishing websites. The suggested method for identifying phishing websites based on features of URL anomalies uses the Gradient Boost Classifier model.The study's findings demonstrate that the suggested method effectively and instantly distinguished between phony and authentic websites.},
        keywords = {Phishing Attacks, Machine Learning, Gradient Boosting, Detection.},
        month = {},
        }