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@article{201054,
        author = {Mrs.M.P.Ruby and Elakkiya.B and Elandhiya.E and Harini.S and Nishanthini.V},
        title = {AI-Based Network Intrusion Detection and Multi-Class Attack Classification System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {271-278},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201054},
        abstract = {With the rapid expansion of computer networks and internet-based services, cybersecurity threats have increased significantly. Traditional intrusion detection systems mainly rely on static rules and signature-based techniques, which are ineffective against emerging and unknown attacks. This paper presents an Artificial Intelligence-based Network Intrusion Detection System capable of detecting and classifying multiple types of network attacks in real time. The proposed system utilizes machine learning algorithms to analyze network traffic patterns and classify them into various attack categories including DoS, Probe, R2L, U2R, and normal traffic. The system integrates real-time packet monitoring, automated alert generation, database logging, and visualization dashboard. Experimental results demonstrate high accuracy and improved detection performance. The proposed system provides an efficient and scalable solution for modern network security.},
        keywords = {Intrusion Detection, Machine Learning, Network Security, Cybersecurity, Multi-Class Classification, Real-Time Monitoring},
        month = {May},
        }