Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines

TL;DR

This paper presents a quantum-hybrid SVM approach for anomaly detection in cyber-physical systems, demonstrating improved accuracy over classical methods in identifying cyber-attacks from sensor data.

Contribution

It introduces a novel quantum-hybrid SVM leveraging quantum fidelity for high-dimensional data analysis, enhancing anomaly detection capabilities in cyber-physical security.

Findings

Achieved an F-1 score of 0.86 on the HAI CPS dataset.

Attained 87% accuracy, outperforming classical methods by 14%.

Effectively processed high-dimensional sensor data using quantum kernels.

Abstract

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.