An intelligent approach for detection and classification of security attacks in a Passive Optical Network using Light Gradient Boosting Machine

Abstract

Over the past decade, Passive Optical Networks (PONs) have emerged as a leading solution for next-generation broadband access, providing high-speed and cost-effective communication. However, PONs face significant security challenges, including data interception, denial-of-service (DoS) attacks, and resource exhaustion caused by malicious Optical Network Units (ONUs). Machine learning (ML), particularly advanced models like Light Gradient Boosting Machine (LightGBM), has proven to be a promising solution for managing complex security issues in PONs. Leveraging its ability to handle imbalanced, high-dimensional datasets, LightGBM was employed in this study to detect and classify malicious ONUs based on bandwidth usage patterns. The model achieved an impressive accuracy of 95.27%, a Matthews Correlation Coefficient (MCC) of 90%, and a precision rate of 93%. While traditional classifiers, such as Naïve Bayes (NB), achieved an accuracy of 88.53%, LightGBM demonstrated superior robustness in addressing class imbalance and enhancing detection accuracy. This work highlights the potential of LightGBM in enhancing PON security and enabling intelligent, resilient broadband networks.