An Intelligent Framework for Network Traffic intrusion detection Using machine Learning techniques

Author(s)	Ms. Harshitha T A, Mr. Suthan R
Country	India
Abstract	The rapid evolution of large-scale network infrastructures has increased the difficulty of detecting cyber intrusions in real time. This paper proposes a machine learning–based Network Intrusion Detection System created utilizing the UNSW-NB15 dataset, which represents realistic network traffic and multiple attack categories. Data preprocessing techniques, including normalization, noise elimination, and class balance correction, are applied to improve learning reliability. Key traffic features are selected to reduce dimensional complexity and enhance computational efficiency. A Light Gradient Boosting Machine (Light GBM) classifier is employed due to its fast convergence, low resource consumption, and strong predictive capability. System Standard intrusion detection metrics are used to evaluate performance effective attack detection with a low false alarm rate, confirming the suitability of Light GBM for efficient and scalable real-time network security applications.
Keywords	Intrusion Detection System, Network Traffic Classification, Gradient Boosting, LightGBM, Cybersecurity Analytics, Data-Driven Security
Field	Computer > Network / Security
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-29

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International Journal For Multidisciplinary Research