Evaluation of a GAN-Based Method for Anomaly Detection in Open RAN Based on Experimental 5G Data

Open RAN introduces substantial flexibility and openness to radio access networks, offering significant advantages while simultaneously increasing vulnerability to complex anomalies and security threats. Although a range of anomaly detection methods exists for other domains, few have been systematically assessed in the context of Open RAN.In this paper, we adapt, employ and analyze a generative adversary network (GAN) model originally designed for unsupervised anomaly detection in multivariate time series data, applying it to the Open RAN domain, using a publicly available experimental 5G data set collected from the OpenIreland 5G testbed. We demonstrate how the GAN model combines a generative approach, using Long Short-Term Memory (LSTM) networks for reconstruction, with a discriminative approach, also based on LSTM, for classification. This integration enables the model to effectively capture complex temporal correlations within the key performance indicators (KPIs) of Open RAN. This method yields an anomaly score, which merges reconstruction error with the discriminator’s output to identify anomalies in real-time.Our findings demonstrate that GAN achieves good accuracy in detecting malicious activities, showcasing good precision and recall performance. Furthermore, we discuss practical considerations for deploying GAN as a security xApp within the near-real-time RAN Intelligent Controller (RIC), addressing aspects such as latency, scalability, and integration with the O-RAN architecture.To the best of our knowledge, this is the first study to apply GAN-based anomaly detection on the publicly available dataset from the OpenIreland 5G testbed.