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@article{191829,
        author = {Aditi Raj and Ekta Bhati and Khushi Sharma},
        title = {Comparative Analysis of Machine Learning Algorithms for Phishing Website Detection Using Random Forest},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {171-172},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191829},
        abstract = {Phishing attacks have become a major cybersecurity concern, exploiting users’ trust to steal confidential information. Traditional blacklist and heuristic-based detection systems fail to recognize novel phishing techniques that evolve rapidly. This study employs machine learning (ML) to improve phishing detection accuracy using URL-based features. Three supervised ML algorithms—Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM)—are compared based on performance metrics such as accuracy, false positive rate, and false negative rate. A dataset of 36,711 URLs (19,653 phishing and 17,058 legitimate) was analyzed using 16 lexical and domain-based features, including domain age, HTTPS presence, and URL length. Experimental findings demonstrate that Random Forest achieves the highest detection accuracy of 97.6%, outperforming other models due to its ensemble learning capability and resistance to overfitting. The proposed system effectively distinguishes phishing URLs from legitimate ones and can be deployed as a browser-based detection module. Future work will integrate hybrid deep learning models for real-time zero-day phishing detection and adaptive feature selection to enhance scalability and robustness.},
        keywords = {Cybersecurity, Decision Tree, Machine Learning, Phishing Detection, Random Forest, Support Vector Machine},
        month = {},
        }