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@article{183020,
        author = {Aniket Doifode and Mrunali Jibhakate and Swaraj Deshmukh},
        title = {Network Security System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {4409-4412},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183020},
        abstract = {Phishing attacks remain one of the most prevalent and evolving cyber threats, targeting users by mimicking legitimate websites. This paper presents a Network Security System that leverages machine learning and MLOps to detect phishing URLs in real time. A Random Forest Classifier trained on a dataset of 11,000 + labeled URLs achieved a classification accuracy of 96.4%, precision of 95.2%, and an AUC-ROC score of 97.1%. The system’s deployment pipeline includes MLflow, DVC, Docker, and GitHub Actions for experiment tracking, data versioning, and CI/CD. The solution features a web interface for real-time detection and MongoDB Atlas for logging and audit. The system addresses real-world scalability, automation, and continuous learning, marking a significant step toward intelligent cybersecurity solutions.},
        keywords = {},
        month = {September},
        }