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@article{202393,
        author = {M.Prashanthi and P.Siva and CH.Harshini and N.Abhiram},
        title = {Phshing Url Detection Through Login Url's - A Real Case Scenario},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7476-7485},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202393},
        abstract = {Phishing attacks are one of the most common cyber threats in modern internet environments. Attackers create fraudulent websites that mimic legitimate plat-forms to steal sensitive information such as login cre-dentials, financial data, and personal details. Tradition-al phishing detection techniques rely on blacklists and rule-based systems, which often fail to detect newly gen-erated phishing URLs.
This paper presents a machine learning–based phishing URL detection system integrated with a Django web framework. The proposed system extracts structural and domain-based features from URLs such as URL length, presence of IP address, number of subdomains, suspicious characters, HTTPS usage, and domain age. These features are used to train multiple machine learn-ing models including Logistic Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest.
Experimental results show that the Random Forest classifier achieves the best performance with an accura-cy of 96.2%. The trained model is integrated into a Django web application where users can enter suspi-cious URLs and receive instant predictions. The system also stores prediction history for monitoring and analy-sis.
This solution provides an efficient and accessible way to protect users from phishing attacks and improve cyber-security awareness.},
        keywords = {Phishing Detection, Machine Learning, URL Analysis, Random Forest, Cybersecurity, Django Framework.},
        month = {May},
        }