用量子增强的机器学习，实现O-RAN网络分层威胁检测
Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)
- 分三层检测：异常识别、入侵确认、多攻击分类
- 近满分准确率，高召回率，类间区分度强
- 适合需要可解释性与实时部署的5G安全场景
开放无线接入网（O-RAN）通过模块化和细粒度遥测提升灵活性，但也扩大了跨控制、用户和管理平面的网络安全攻击面。本文提出一种分层防御框架，包含异常检测、入侵确认和多攻击分类三个协同层级，与O-RAN遥测架构对齐。方法融合混合量子计算与机器学习，采用幅度和纠缠基特征编码，结合深度与集成分类器。在合成与真实遥测数据上进行广泛基准测试，评估编码深度、架构变体与诊断保真度。框架始终达到接近完美的准确率、高召回率及强类别可分性。通过决策边界、概率裕度与潜在空间几何的多维度评估，验证其可解释性、鲁棒性，具备面向切片感知诊断与近实时及非实时RIC域可扩展部署的准备度。
原文摘要 · Abstract (English)
Open Radio Access Networks (O-RAN) enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers-anomaly detection, intrusion confirmation, and multiattack classification-each aligned with O-RAN's telemetry stack. Our approach integrates hybrid quantum computing and machine learning, leveraging amplitude- and entanglement-based feature encodings with deep and ensemble classifiers. We conduct extensive benchmarking across synthetic and real-world telemetry, evaluating encoding depth, architectural variants, and diagnostic fidelity. The framework consistently achieves near-perfect accuracy, high recall, and strong class separability. Multi-faceted evaluation across decision boundaries, probabilistic margins, and latent space geometry confirms its interpretability, robustness, and readiness for slice-aware diagnostics and scalable deployment in near-RT and non-RT RIC domains.
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