VQC-ZTI：面向触觉互联网零信任保护的变分量子控制
VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet
浏览论文内容
中文总结 AI 辅助
本文提出VQC-ZTI框架，通过分平面变分量子分类器将异常评分与执行解耦，在触觉互联网服务中实现零信任保护，其量子神经网络在多留出法评估中表现优异，误报率显著低于ExtraTrees。
中文摘要 AI 辅助
触觉互联网服务将网络事件直接耦合到物理执行，因此安全决策必须在不干扰控制路径的前提下提升风险区分能力。本文提出VQC-ZTI，一种用于触觉互联网服务零信任保护的分平面变分量子分类器（Variational Quantum Classifier, VQC）框架：其中路径外的VQC分析加密流量遥测数据，而路径内的策略引擎执行缓存的确定性授权、限制、升级及弃权（不执行）操作。通过将异常评分与执行解耦，VQC-ZTI可保持可控的控制行为，并允许独立调整检测器灵敏度与策略激进程度。我们采用混合PyTorch-PennyLane实现，基于CESNET衍生的聚合流量，通过随机、实体组及时间留出法开展评估。该全混合量子神经网络在受试者工作特征曲线下的平均面积分别为0.9981、0.9974和0.9941，且相对于ExtraTrees分别降低了44.6%、49.6%和67.9%的误报率。代表性组件时序分解进一步表明，批量VQC评分仍处于异步证据路径而非直接执行路径。
英文摘要
Tactile Internet services couple cyber events directly to physical actuation, so security decisions must improve risk discrimination without perturbing the control path. This paper presents VQC-ZTI, a split-plane Variational Quantum Classifier framework for zero-trust protection of Tactile Internet services, in which an off-path VQC analyzes encrypted-flow telemetry while an on-path policy engine applies cached deterministic grant, restrict, step-up, and deny actions. By decoupling anomaly scoring from enforcement, VQC-ZTI preserves predictable control behavior and allows detector sensitivity and policy aggressiveness to be tuned independently. We evaluate the framework on CESNET-derived aggregated traffic using random, entity-group, and temporal holdouts with a hybrid PyTorch-PennyLane implementation. The full-hybrid Quantum Neural Network achieves mean areas under the receiver operating characteristic curve of 0.9981, 0.9974, and 0.9941 and reduces the false-positive rate relative to ExtraTrees by 44.6%, 49.6%, and 67.9%, respectively. A representative component-timing decomposition further illustrates that batched VQC scoring remains in the asynchronous evidence path rather than the immediate enforcement path.
发表机构
- Iowa State University（爱荷华州立大学）
- Grand Valley State University（大峡谷州立大学）
- Kansas State University（堪萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。
