VWA-6G AI assisted continuous security monitoring over open RAN service management orchestration

Abstract

The evolution towards sixth generation (6G) mobile networks and Open Radio Access Network (O-RAN) architectures introduces enhanced flexibility and scalability but also significantly broadens the cybersecurity threat landscape. Integration of open-source software components and third-party applications (xApps) exacerbates security vulnerabilities, challenging conventional protection mechanisms. To address these issues, this study proposes the Vulnerability Weakness Attack for 6G (VWA-6G) system, an artificial intelligence (AI) assisted framework for continuous security monitoring. This framework utilizes a contextually fine-tuned BERT-based model. The VWA-6G AI model automates semantic mapping from Common Vulnerabilities and Exposures (CVEs) to Common Weakness Enumerations (CWEs) and Common Attack Pattern Enumerations and Classifications (CAPECs), leveraging specialized datasets derived from forward-looking 6G technical materials. Empirical results demonstrate that the proposed model achieves superior performance metrics compared to baseline methods, notably an accuracy of 98.62 % and an F1-Score of 99.44 %, representing significant improvements over standard BERT and V2W-BERT approaches. This AI driven semantic approach substantially enhances vulnerability identification and mapping accuracy, thereby providing robust, automated, and proactive security management aligned with Zero Trust principles in 6G O-RAN environments.