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@article{171525,
        author = {Mittapally Varsha and Vaddeman Suresh and Mangali Sambhavana and Sapavath Yakub and Zeenath},
        title = {Enhancing Malicious URL Detection with Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {531-536},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171525},
        abstract = {The increasing reliance on the internet has heightened exposure to cyber threats, with malicious URLs being a significant concern. This research focuses on building a machine learning-based system to detect such URLs effectively. A Random Forest classifier is utilized, leveraging critical features like URL length, special character frequency, and the presence of IP addresses to identify harmful URLs. The system is integrated into a user-accessible web application developed using Flask, enabling real-time URL analysis. Users can input a URL, and the system evaluates its safety, categorizing it as either "safe to proceed" or "not safe to proceed." By training the model on a labeled dataset, the system ensures accurate differentiation between benign and malicious URLs. This solution offers a faster and more efficient means of safeguarding users during web browsing, showcasing the potential of machine learning in enhancing cybersecurity.},
        keywords = {},
        month = {January},
        }