Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines
This addresses security risks in critical infrastructures like cyber-physical systems, but it is incremental as it builds on existing quantum and classical techniques.
The paper tackled anomaly detection in cyber-physical systems using a quantum-hybrid SVM, achieving an F-1 score of 0.86 and 87% accuracy on the HAI CPS dataset, performing equally to existing work and 14% better than classical methods.
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.