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@article{179150,
        author = {Amsaleka R and Deepika M and Dharshini R and Induja S and Yogeshwari S},
        title = {Phishing Website Detection using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6333-6337},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179150},
        abstract = {Phishing attacks continue to pose significant threats to online users by mimicking legitimate websites to steal sensitive information. This paper presents a machine learning-based approach for the detection and classification of phishing websites using a combination of supervised learning algorithms. Various features, including URL characteristics, domain identity, and webpage content, are extracted and analyzed. The study evaluates the performance of classifiers such as Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression on a benchmark phishing dataset. Experimental results demonstrate that ensemble models, particularly Random Forest, achieve superior accuracy and robustness in identifying phishing websites. The findings highlight the effectiveness of machine learning in enhancing web security through early detection and prevention of phishing attacks.},
        keywords = {Phishing attack, Machine learning},
        month = {May},
        }