自适应检测5G网络干扰，无需人工干预
A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
- 基于机器学习的闭环检测框架，实时识别干扰信号
- 在多种未知干扰场景下准确率和适应性均优于现有方法
- 适合5G O-RAN安全团队及智能网络运维人员
开放无线接入网（O-RAN）通过软件定义网络、网络功能虚拟化和标准化开放接口，实现了模块化、智能化与可编程的5G网络架构。然而，干扰攻击是O-RAN面临的重要安全威胁，会严重损害网络性能。本文提出SAJD——一种集成于AI/ML框架的自适应干扰检测框架，可在无须人工干预的情况下自主检测O-RAN环境中的干扰攻击。SAJD构建了一个闭环系统，包含由我们开发的基于机器学习的xApp实现近实时的射频信号干扰推理，以及通过rApps实现的持续监控与重训练流水线。本演示将展示SAJD在符合O-RAN标准的测试平台中，面对多种动态且此前未见的干扰场景时，其准确率和适应性均优于现有基于离线训练与人工标注的xApp。
原文摘要 · Abstract (English)
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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