VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet

2026-08-19

AI Summary

本文提出VQC-ZTI框架，利用变分量子分类器保护触觉互联网服务安全，通过分离异常评分与执行路径来提高安全性并保持控制行为可预测性。

Abstract

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.

Problem

• Tactile Internet
• Zero Trust
• Security Decisions
• Risk Discrimination
• Control Path

Innovation

• Variational Quantum Classifier
• Zero Trust Protection
• Tactile Internet
• Anomaly Scoring Decoupling
• Hybrid Quantum Neural Network