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@article{206423,
        author = {Laxman Ganpule},
        title = {Machine Learning Driven Cyber Intrusion Detection and Visualization System Using NSL-KDD Dataset},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2010-2013},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206423},
        abstract = {The rapid expansion of digital communication and services over the Internet has led to a notable rise in the frequency and sophistication of cyberattacks. Traditional Intrusion Detection Systems (IDS) frequently fail to detect modern sophisticated threats due to their reliance on static signatures and limited adaptability. This study proposes a Machine Learning Driven Cyber Intrusion Detection and Visualisation System using NSL-KDD dataset for intelligent network attack detection and real-time monitoring. The proposed system integrates supervised machine learning algorithms (Decision Tree and Random Forest) with a real-time detection engine and an interactive visualisation dashboard developed using Flask and Chart.js. The system performs data pre-processing, encoding of categorical features, attack classification, confidence aware prediction, live logging and service level attack analysis. Intrusions are classified into various attack classes such as Denial of Service (DoS), Probe, Remote-to-Local (R2L), User-to-Root (U2R), Unknown and Normal traffic. The experimental results show that the Random Forest algorithm is better than Decision Tree in detection performance. The dashboard provides real-time monitoring, graphical visualisation, and threat analytics to enhance cybersecurity operations},
        keywords = {},
        month = {July},
        }