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@article{175659,
        author = {Dr. Sunil Wankhade and Hritik Kanse and Aditya Mohile and Sanika Padme and Riya Sawant},
        title = {Phishing Website Detection Using ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4159-4165},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175659},
        abstract = {Phishing websites are a significant security threat. Numerous cyberattacks jeopardize the confidentiality, integrity, and availability of both company and consumer data, with phishing often being the initial step in these attacks. Optical Character Recognition (OCR) has emerged as a viable solution for real-time detection of phishing links on mobile platforms.[1] Researchers have dedicated decades to developing innovative methods for the automatic detection of phishing sites. Although advanced solutions can yield improved outcomes, they often require extensive manual feature engineering and struggle to identify new phishing tactics. Consequently, there remains a pressing need for strategies that can automatically detect phishing websites and swiftly address zero- day phishing attempts. The webpage linked in the URL contains a wealth of information that can help assess the maliciousness of the web server. Machine Learning has proven to be an effective approach for phishing detection, overcoming the limitations of previous methods. We performed a comprehensive literature review and proposed a novel technique for identifying phishing websites through feature extraction and a machine learning algorithm. This research aims to utilize the collected dataset to train machine learning models and deep neural networks to predict phishing websites.},
        keywords = {Phishing Website, Fake Website, Spam, Hacker, Verification, Action, Document, Urgent, Message, Password},
        month = {April},
        }