Comparing Boosting-Based and GAN-Based Models for Intrusion Detection in 5G Networks

The rise of 5G networks has been driven by the increasing deployment of Internet of Things (IoT) devices and the expansion of mobile and fixed broadband subscriptions. This has been coupled with a rise in network-related attacks, driven by the expanding attack surfaces. Machine learning (ML) has emerged as a promising solution for detecting security threats in 5G-enabled networks and environments due to its ability to handle the vast amount of data generated. Two ML model types have shown great promise, namely boosting-based and Generative Adversarial Network (GAN) based models. Accordingly, this work proposed a comparative analysis of boosting-based and GAN-based ML models for intrusion detection in softwarized 5G networks. Experimental results using the 5G-NIDD dataset show that both boosting-based and GAN-based models have a high detection capability, are not significantly impacted by feature selection, and have reduced training and prediction times.