Robust Intrusion Detection System with Explainable Artificial Intelligence

Machine Learning (ML) models are widely adopted for threat detection and mitigation, but their susceptibility to adversarial inputs presents new vulnerabilities, particularly in time-sensitive environments like 6G and Open Radio Access Network (O-RAN). Existing defenses, such as adversarial training, are resource intensive and often fail in real-time scenarios. To address this, we propose a novel adversarial detection and mitigation framework that leverages eXplainable Artificial Intelligence (XAI) to provide real-time insights and automated zero-touch responses. Our method is integrated into Intrusion Detection Systems (IDS) and validated through extensive testing in the Radio Resource Control (RRC) layer of the O-RAN framework. Experimental results demonstrate improved detection accuracy and reduced response time compared to baseline approaches, showcasing the effectiveness of XAI-enhanced zero-touch security in dynamic network environments.