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

• URL: http://arxiv.org/abs/2409.04935v1
• Date: Sun, 8 Sep 2024 00:15:30 GMT
• Title: Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines
• Abstract summary: Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. The application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. We 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.
• Score: 0.3749861135832072
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics [54.3410887955176]
  Crime pattern analysis is critical for law enforcement and predictive policing.<n>The surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced datasets.<n>This study presents a quantum-classical comparison framework for crime analytics.
  arXiv  Detail & Related papers  (2026-04-08T06:05:25Z)
• EAQKD: Entanglement-Based Authenticated Quantum Key Distribution [0.0]
  Entanglement-Based Authenticated Quantum Key Distribution (EAQKD) is a novel protocol that addresses critical security and practical limitations in quantum cryptographic key exchange.<n>We evaluate EAQKD's performance through a comprehensive discrete-event simulation framework.<n>Our results show quantum bit error rates consistently below the 11% security threshold.
  arXiv  Detail & Related papers  (2026-03-02T20:30:34Z)
• Noise-Resistant Feature-Aware Attack Detection Using Quantum Machine Learning [43.509065918669314]
  Continuous-variable quantum key distribution (CV-QKD) is a quantum communication technology that offers an unconditional security guarantee.<n>We propose a quantum machine learning (QML)-based attack detection framework (QML-ADF) that safeguards the security of high-rate CV-QKD systems.
  arXiv  Detail & Related papers  (2026-01-11T03:40:45Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Privacy-Aware Framework of Robust Malware Detection in Indoor Robots: Hybrid Quantum Computing and Deep Neural Networks [5.574636535424702]
  We propose a privacy-aware malware detection framework for indoor robotic systems.<n>Our architecture achieves up to 95.2% detection accuracy under privacy-constrained conditions.<n>This work advances trustworthy AI for secure, autonomous CPS operations.
  arXiv  Detail & Related papers  (2025-10-15T04:25:33Z)
• Performance and Storage Analysis of CRYSTALS Kyber as a Post Quantum Replacement for RSA and ECC [49.1574468325115]
  CRYSTALS-Kyber is a post-quantum cryptographic solution standardized by NIST in 2022.<n>This study evaluates Kyber's practical viability through performance testing across various implementation schemes.
  arXiv  Detail & Related papers  (2025-08-03T09:53:45Z)
• FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report [36.1999598554273]
  This report investigates and compares the efficacy of classical, quantum, and quantum-hybrid machine learning models for the binary behavioural classification of fraudulent financial activities.<n>We implement and evaluate a range of models on the IBM Anti-Money Laundering (AML) dataset.<n>We propose Fraud Detection for Quantum Computing (FD4QC), a practical, API-driven system architecture designed for real-world deployment.
  arXiv  Detail & Related papers  (2025-07-25T16:08:22Z)
• Calibration of Quantum Devices via Robust Statistical Methods [45.464983015777314]
  We numerically analyze advanced statistical methods for Bayesian inference against the state-of-the-art in quantum parameter learning.<n>We show advantages of these approaches over existing ones, namely under multi-modality and high dimensionality.<n>Our findings have applications in challenging quantumcharacterization tasks namely learning the dynamics of open quantum systems.
  arXiv  Detail & Related papers  (2025-07-09T15:22:17Z)
• Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems [0.3749861135832072]
  This study focuses on the parameterization of Quantum Hybrid Support Vector Machines (QSVMs) using three popular datasets from Cyber-Physical Systems (CPS)<n>Results demonstrate that QSVMs outperform traditional classical kernel methods, achieving 13.3% higher F1 scores.<n>This effort suggests that QSVMs can provide a substantial advantage in anomaly detection for ICS, ultimately enhancing the security and integrity of critical infrastructures.
  arXiv  Detail & Related papers  (2025-06-21T21:37:26Z)
• Gigabit-rate Quantum Key Distribution on Integrated Photonic Chips [17.76925769483522]
  Quantum key distribution (QKD) provides information-theoretic security guaranteed by the laws of quantum mechanics. We report an integrated silicon photonics-based QKD system that achieves a secret key rate of 1.213 Gbit per second over a metropolitan distance of 10 km with polarization multiplexing.
  arXiv  Detail & Related papers  (2025-04-11T06:57:16Z)
• Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems [42.184783937646806]
  We investigate the potential impact of quantum computing and machine learning (QML) on cybersecurity applications of traditional ML.<n>First, we explore the potential advantages of quantum computing in machine learning problems specifically related to cybersecurity.<n>Then, we describe a methodology to quantify the future impact of fault-tolerant QML algorithms on real-world problems.
  arXiv  Detail & Related papers  (2025-02-16T15:49:25Z)
• A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor [0.0]
  We propose a novel quantum machine learning method, called. the-Efficient Quantum Anomaly Detection (PEQAD), for practical image anomaly detection. PEQAD aims to achieve both parameter efficiency and superior accuracy compared to classical models. We demonstrate the first implementation of a quantum anomaly detection method for general image datasets on a superconducting quantum processor.
  arXiv  Detail & Related papers  (2024-12-22T05:36:51Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Parametrized Energy-Efficient Quantum Kernels for Network Service Fault Diagnosis [0.49157446832511503]
  We show significant performance improvements and an efficient achievement of high performance over conventional methods. Experimental validation of the quantum kernel was conducted using IBM's superconducting quantum computer IBM-Kawasaki.
  arXiv  Detail & Related papers  (2024-05-15T23:06:47Z)
• Quantum-activated neural reservoirs on-chip open up large hardware security models for resilient authentication [5.60384815385868]
  This work implements a large-scale quantum-activated recurrent neural network possessing more than 3 trillion hardware nodes/cm$2$. The chip supports a bit density per feature size area three times higher than the best technology available, with the capacity to store more than $21104$ keys in a footprint of 1 cm$2$.
  arXiv  Detail & Related papers  (2024-03-21T07:25:52Z)
• Neural auto-designer for enhanced quantum kernels [59.616404192966016]
  We present a data-driven approach that automates the design of problem-specific quantum feature maps. Our work highlights the substantial role of deep learning in advancing quantum machine learning.
  arXiv  Detail & Related papers  (2024-01-20T03:11:59Z)
• Optical Quantum Sensing for Agnostic Environments via Deep Learning [59.088205627308]
  We introduce an innovative Deep Learning-based Quantum Sensing scheme. It enables optical quantum sensors to attain Heisenberg limit (HL) in agnostic environments. Our findings offer a new lens through which to accelerate optical quantum sensing tasks.
  arXiv  Detail & Related papers  (2023-11-13T09:46:05Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection [3.955274213382716]
  Anomaly detection in Restricted Detection and Response (EDR) is a critical task in cybersecurity programs of large companies. Classical machine learning approaches to this problem exist, but they frequently show unsatisfactory performance in differentiating malicious from benign anomalies. A promising approach to attain superior generalization than currently employed machine learning techniques are quantum generative models.
  arXiv  Detail & Related papers  (2023-06-08T07:36:01Z)
• Experimental quantum adversarial learning with programmable superconducting qubits [15.24718195264974]
  We show the first experimental demonstration of quantum adversarial learning with programmable superconducting qubits. Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios.
  arXiv  Detail & Related papers  (2022-04-04T18:00:00Z)
• Quantum Support Vector Machines for Continuum Suppression in B Meson Decays [0.27342795342528275]
  We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703.
  arXiv  Detail & Related papers  (2021-03-23T02:09:05Z)
• Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
  We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations. We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
  arXiv  Detail & Related papers  (2020-12-08T01:10:30Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.