6G-XSec: Explainable Edge Security for Emerging OpenRAN Architectures

The evolution from 5G to 6G cellular networks signifies a crucial advancement towards enhanced robustness and automation driven by the promise of ubiquitous Artificial Intelligence (AI) to overhaul network operations, commonly referred to as AIOps. However, 6G network operators also need to deal with evolving threats at the edge to ensure data integrity and availability. We introduce 6G-XSEC, the first framework that seeks to automatically monitor, analyze, and explain anomalies and threats at the cellular network edge. Our framework enhances the emerging Open Radio Access Network (O-RAN) control plane with run-time analytic capabilities and explainability. A distinguishing aspect of our framework is the use of expert referencing, a coupling of lightweight unsupervised deep learning-based anomaly detection with large language models (LLMs) to first detect, analyze, and subsequently explain complicated real-world cellular threats and anomalies at run-time, based on enhanced security telemetry from the O-RAN data plane. We build a prototype 6G-XSEC framework and evaluate it against 5 end-to-end cellular attacks from the literature, achieving 100% detection rate with our best model. We also propose effective LLM prompt templates for attack analysis and present qualitative results from 5 popular LLMs.