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@article{202561,
        author = {Premraj Yuvaraj Masule and Yash  Meghanad Bagul and Purvesh Pravin Pawar and Prashant Mansingh Patil and Dr. Surekha Hitendra Patil},
        title = {A Real-Time Network Intrusion Detection System Using Ensemble Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7085-7090},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202561},
        abstract = {The rapid growth of cyber threats has increased the demand for intelligent intrusion detection systems capable of monitoring network traffic in real time. This paper presents a real-time Network Intrusion Detection System (NIDS) that combines ensemble machine learning with live traffic monitoring and streaming architecture. The proposed framework captures network packets using Scapy, extracts flow-based traffic features inspired by the CICIDS2017 dataset, and classifies traffic using XGBoost and Random Forest classifiers. The system integrates a FastAPI backend, WebSocket communication, and a React.js dashboard for real-time alert visualization and traffic monitoring. Percentage of results demonstrate strong detection performance with low inference latency, making the framework suitable for practical cybersecurity applications.},
        keywords = {Intrusion Detection System, Machine Learning, XGBoost, Random Forest, CICIDS2017, Cybersecurity, Real-Time Monitoring},
        month = {May},
        }