Network Traffic Intrusion Detection

The rapid growth and complexity of today's net-work environments have made intelligent and efficient intrusion detection more important than ever.This study explores the development of a high-performance Network Traffic Intrusion Detection System (IDS) using the UNSW-NB15 dataset-a mod-ern benchmark designed to reflect realistic network behavior and a broad range of cyberattacks.To ensure reliable model training, the dataset is carefully preprocessed through data cleaning, normalization, and techniques for handling class imbalance.Feature extraction and selection are applied to pinpoint the most informative attributes, reducing dimensionality and improving computational efficiency.A Light Gradient Boosting Machine (LightGBM) classifier is chosen for its ability to learn complex patterns quickly and efficiently, with minimal memory require-ments.The model's performance is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, and false positive rate.Experimental findings reveal that the LightGBMbased IDS delivers strong detection accuracy and successfully differentiates normal traffic from various intrusion types.Overall, this research emphasizes the value of pairing con-temporary datasets with powerful gradient-boosting algorithms to create reliable and scalable IDS solutions.The results indicate that LightGBM provides a well-balanced trade-off between speed and predictive power, making it an effective choice for real-time intrusion detection.Ultimately, this study demonstrates how data-driven machine learning techniques can play a pivotal role in reinforcing cybersecurity defenses against evolving network threats.