mlsanityData to 14 Oct 2026

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

arXiv 2409.04935 ↗First posted 8 Sep 2024quant-ph cs.CR cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 8 Sep 2024.

Builds on

6 of 22 references.

References · 6

  1. 2018Supervised learning with quantum enhanced feature spacescited by 665
  2. 2020Robust data encodings for quantum classifierscited by 91
  3. 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
  4. 2023Unravelling physics beyond the standard model with classical and quantum anomaly detectioncited by 9
  5. 2023Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernelcited by 6
  6. 2021Simultaneous execution of quantum circuits on current and near-future NISQ systemscited by 5

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurationscited by 2
  2. 2025Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detectioncited by 1
  3. 2025Quantum Hybrid Support Vector Machines for Stress Detection in Older Adultscited by 0
  4. 2025Detection of Physiological Data Tampering Attacks with Quantum Machine Learningcited by 0
  5. 2025Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systemscited by 0