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

2025-06-21

AI Summary

To address insufficient accuracy in detecting anomalies involving sensitive data within industrial control systems (ICS), this paper proposes a quantum-hybrid support vector machine (QSVM) method: a quantum kernel function is embedded into the classical SVM framework, and its robustness is empirically validated under simulated hardware noise on real IBM Q quantum devices. Experiments on three representative cyber-physical system datasets demonstrate that the proposed method improves kernel target alignment (KTA) by 91.023% and achieves a 13.3% higher F1-score than classical SVM. Under realistic quantum hardware noise, performance degrades by only 1.57% on average—significantly outperforming conventional approaches. This work constitutes the first systematic empirical validation of quantum kernels for ICS anomaly detection, establishing their practical utility and noise resilience. It provides a deployable pathway toward quantum-enhanced cybersecurity for critical infrastructure.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Detecting anomalies in Industrial Control Systems using quantum-hybrid SVMs
• Improving anomaly detection accuracy with quantum kernel methods
• Assessing quantum advantage in cyber-physical system security

Innovation

• Quantum-Hybrid SVM for anomaly detection
• Quantum kernels enhance feature spaces
• Noise-resistant QSVM with high accuracy