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

2025-10-09

AI Summary

O-RAN’s openness and intelligence introduce vulnerability to adaptive interference attacks, compromising network stability. To address this, we propose SAJD—the first closed-loop adaptive interference detection framework tailored for O-RAN. SAJD integrates a machine learning–based xApp for real-time inference with an rApp-driven online retraining pipeline, enabling near-real-time interference detection and continuous self-optimization. Fully automated, it autonomously executes the detection–feedback–retraining cycle without human intervention, even under dynamic and previously unseen interference conditions. Evaluated on an O-RAN-compliant testbed, SAJD significantly improves detection accuracy over conventional offline-trained methods while markedly enhancing environmental adaptability and robustness against evolving interference patterns.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: State Estimation
• Machine Learning: Online Learning & Bandits

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Detects self-adaptive jamming attacks in AI/ML integrated O-RAN networks
• Autonomously identifies radio signal interference without human intervention
• Outperforms existing methods in accuracy under dynamic interference scenarios

Innovation

• Self-adaptive jammer detection framework for O-RAN
• Closed-loop system with ML-based xApp inference
• Continuous monitoring and retraining through rApps