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@article{193114,
        author = {Jesy Jeff Laura E and Aditi Gupta},
        title = {PhishCure: AI Powered Phishing Link Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {4129-4135},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193114},
        abstract = {Phishing attacks are fraudulent communication techniques used to deceive users into revealing sensitive information by impersonating trusted entities. These attacks are commonly delivered through email and Short Message Service (SMS), leading to identity theft, financial loss, malware infections, and reputational damage. This paper presents PhishCure: Phishing Detection with Artificial Intelligence, a web-based system that leverages machine learning to identify malicious Uniform Resource Locators (URLs). The system is trained on a dataset containing legitimate and confirmed phishing URLs collected from various sources. Important features such as domain characteristics, Internet Protocol (IP) address usage, domain age, security protocol, and structural URL attributes are extracted and analyzed. A Random Forest classifier is employed to evaluate the likelihood of a URL being malicious. Based on the prediction results, the system provides warning alerts for suspicious URLs and trust indicators for legitimate ones. The proposed solution enhances cybersecurity defenses by enabling real-time phishing detection and protecting users from online threats.},
        keywords = {Artificial Intelligence, Cybersecurity, Machine Learning, Phishing Detection, Random Forest, URL Analysis.},
        month = {February},
        }