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.

翻译：信息物理控制系统是关键基础设施，其设计围绕高度响应的反馈回路，由数百个传感器和控制器进行测量与操控。异常数据（例如来自网络攻击的数据）对基础设施及操作人员的安全构成重大风险。随着量子计算范式的最新进展，量子技术在异常检测中的应用可极大提升对物理传感器数据中网络攻击的识别能力。本文探讨了采用强预处理方法与量子混合支持向量机（SVM）的结合，该SVM利用参数化量子电路中的保真度特性，以高效且有效的方式对极高维数据进行降维处理。我们的实验结果显示，在HAI CPS数据集上，使用8量子比特、16特征的量子核函数取得了0.86的F1分数和87%的准确率，其性能与现有研究持平，且较经典对应方法提升14%。