Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems

TL;DR

This paper demonstrates that Quantum Hybrid Support Vector Machines outperform classical methods in anomaly detection for Industrial Control Systems, showing higher accuracy, robustness to noise, and potential quantum advantage.

Contribution

It introduces a parameterized QSVM approach for ICS anomaly detection and compares its performance with classical methods using real quantum hardware simulations.

Findings

QSVMs achieve 13.3% higher F1 scores than classical kernels.

Maximum noise error in QSVM kernels is 0.98%.

Kernel-target alignment improves by 91.023% with QSVMs.

Abstract

Sensitive data captured by Industrial Control Systems (ICS) play a large role in the safety and integrity of many critical infrastructures. Detection of anomalous or malicious data, or Anomaly Detection (AD), with machine learning is one of many vital components of cyberphysical security. Quantum kernel-based machine learning methods have shown promise in identifying complex anomalous behavior by leveraging the highly expressive and efficient feature spaces of quantum computing. This study focuses on the parameterization of Quantum Hybrid Support Vector Machines (QSVMs) using three popular datasets from Cyber-Physical Systems (CPS). The results demonstrate that QSVMs outperform traditional classical kernel methods, achieving 13.3% higher F1 scores. Additionally, this research investigates noise using simulations based on real IBMQ hardware, revealing a maximum error of only 0.98% in the QSVM kernels. This error results in an average reduction of 1.57% in classification metrics. Furthermore, the study found that QSVMs show a 91.023% improvement in kernel-target alignment compared to classical methods, indicating a potential "quantum advantage" in anomaly detection for critical infrastructures. This effort suggests that QSVMs can provide a substantial advantage in anomaly detection for ICS, ultimately enhancing the security and integrity of critical infrastructures.