π€ AI Summary
This study addresses security vulnerabilities and anomaly detection challenges in next-generation cellular networks by proposing an explainable AI-based anomaly detection framework for O-RAN. By integrating post-hoc explanation methods to identify Key Performance Metrics (KPMs), the approach enables precise malicious traffic identification and attack characterization within a real-world O-RAN testbed. The proposed method reduces data complexity by 80% while maintaining high detection accuracy, effectively balancing computational efficiency, detection performance, and model interpretability. Consequently, this work significantly enhances the security resilience and practical deployment feasibility of NextG networks, offering a robust solution for identifying sophisticated threats in open radio access network environments without compromising operational efficiency or transparency.
π Abstract
The wireless networks have historically faced significant security vulnerabilities, necessitating advanced anomaly detection mechanisms, especially as networks evolve towards 6G and beyond. This study introduces an advanced anomaly detection framework that leverages explainable artificial intelligence to enhance the security of next-generation (NextG) cellular networks. By implementing and evaluating a variety of artificial intelligence models, the framework demonstrates high accuracy and efficient runtime performance in identifying malicious traffic within a realistic Open Radio Access Network (O-RAN) testbed. A key innovation of this work is the integration of post-hoc explainability methods to identify the most critical key performance metrics (KPMs), which enables a significant 80% reduction in dataset complexity without compromising detection accuracy. Additionally, explainability analyses identify several critical attack traffic characteristics, such as protocol type, bandwidth, interval, and duration, to prevent upcoming network attacks. The resulting framework effectively balances computational efficiency, accuracy, and explainability, underscoring its practical applicability for enhancing security in next-generation cellular networks.