A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN

• URL: http://arxiv.org/abs/2510.09706v1
• Date: Fri, 10 Oct 2025 00:18:00 GMT
• Title: A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
• Authors: Md Habibur Rahman, Md Sharif Hossen, Nathan H. Stephenson, Vijay K. Shah, Aloizio Da Silva,
• Abstract summary: jamming attacks can severely undermine network performance.<n>This paper presents SAJD, a self-adaptive jammer detection framework that autonomously detects jamming attacks.<n>We will show how SAJD outperforms state-of-the-art jamming detection xApp in terms of accuracy and adaptability.
• Score: 2.1698490675188213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming attacks. This paper presents SAJD- a self-adaptive jammer detection framework that autonomously detects jamming attacks in AI/ML framework-integrated ORAN environments without human intervention. The SAJD framework forms a closed-loop system that includes near-realtime inference of radio signal jamming via our developed ML-based xApp, as well as continuous monitoring and retraining pipelines through rApps. In this demonstration, we will show how SAJD outperforms state-of-the-art jamming detection xApp (offline trained with manual labels) in terms of accuracy and adaptability under various dynamic and previously unseen interference scenarios in the O-RAN-compliant testbed.

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