用量子混合SVM检测工业控制系统异常，性能优于经典方法14%。
Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines
- 结合强预处理与量子化核函数的SVM，降低高维传感器数据复杂度。
- 在HAI CPS数据集上达到0.86的F1分数和87%准确率。
- 适合关注量子计算在安全监控中应用的研究者或工程师。
网络物理控制系统依赖数百个传感器和控制器构成的快速反馈回路，其安全性至关重要。来自网络攻击的异常数据会严重威胁系统运行与人员安全。随着量子计算的发展，将量子技术应用于异常检测可显著提升对物理传感器数据中网络攻击的识别能力。本文提出一种结合强预处理方法与量子混合支持向量机（SVM）的方案，利用参数化量子电路的保真度高效压缩极高维度的数据。实验结果表明，在8量子比特、16特征的量子核下，于HAI CPS数据集上获得0.86的F1分数和87%的准确率，性能与现有方法相当，且比经典模型提升14%。
原文摘要 · Abstract (English)
Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controllers. Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. With recent advances in the quantum computing paradigm, the application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. In this paper, we explore the use of strong pre-processing methods and a quantum-hybrid Support Vector Machine (SVM) that takes advantage of fidelity in parameterized quantum circuits to efficiently and effectively flatten extremely high dimensional data. Our results show an F-1 Score of 0.86 and accuracy of 87% on the HAI CPS dataset using an 8-qubit, 16-feature quantum kernel, performing equally to existing work and 14% better than its classical counterpart.
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