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@article{173781,
        author = {V. Rashmi Rakshitha and K. Komali and M. Vinay and G. Avinash and M. Sandeep},
        title = {Phishing Alert System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1521-1525},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173781},
        abstract = {Phishing is a common form of cyberattack where fraudulent websites are used to deceive users into revealing sensitive information. Detecting phishing sites is crucial for enhancing online security. This study presents a machine learning-based system for phishing detection using URL features. A balanced dataset containing phishing and legitimate samples was used to train and evaluate the models. The system extracts 23 essential features from URLs and employs machine learning models such as Random Forest, SVM, Gradient Boosting, and MLP for classification. Among these, the Random Forest model achieved the highest accuracy of 96.17%. The system is deployed as a web application using Flask, providing real-time detection and easy user interaction. This research highlights the effectiveness of machine learning in improving cybersecurity through accurate and efficient phishing detection.},
        keywords = {Phishing Detection, Machine Learning, Random Forest, URL Features, Cybersecurity.},
        month = {March},
        }