SAJD: Self-Adaptive Jamming Attack Detection in AI/ML Integrated 5G O-RAN Networks

TL;DR

This paper presents SAJD, a self-adaptive framework for detecting jamming attacks in AI/ML-enabled 5G O-RAN networks, utilizing real-time inference, continuous monitoring, and dynamic retraining to enhance security and reliability.

Contribution

The paper introduces a novel self-adaptive jamming detection framework that autonomously updates its models in real-time, improving detection accuracy and adaptability in dynamic network environments.

Findings

SAJD outperforms existing offline detection methods in accuracy.

The framework effectively detects previously unseen interference scenarios.

Continuous retraining maintains high detection performance over time.

Abstract

The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking (SDN), network function virtualization (NFV), and implementation of standardized open interfaces. It also facilitates closed loop control and (non/near) real-time optimization of radio access network (RAN) through the integration of non-real-time applications (rApps) and near-real-time applications (xApps). However, one of the security concerns for O-RAN that can severely undermine network performance and subject it to a prominent threat to the security & reliability of O-RAN networks is jamming attacks. To address this, we introduce SAJD-a self-adaptive jammer detection framework that autonomously detects jamming attacks in artificial intelligence (AI) / machine learning (ML)-integrated O-RAN environments. The SAJD framework forms a closed-loop system that includes near-real-time inference of radio signal jamming interference via our developed ML-based xApp, as well as continuous monitoring and retraining pipelines through rApps. Specifically, a labeler rApp is developed that uses live telemetry (i.e., KPIs) to detect model drift, triggers unsupervised data labeling, executes model training/retraining using the integrated & open-source ClearML framework, and updates deployed models on the fly, without service disruption. Experiments on O-RAN-compliant testbed demonstrate that the SAJD framework outperforms state-of-the-art (offline-trained with manual labels) jamming detection approach in accuracy and adaptability under various dynamic and previously unseen interference scenarios.