Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems

2025-12-30

arXiv.org

AI Summary

This work proposes a quantum machine learning–based approach to detect stealthy coordinated cyber-physical attacks in distributed generation systems, which are difficult to identify as they subtly manipulate control and measurement signals to mimic normal operation. A binary classification dataset is constructed using reactive power, frequency deviation, and terminal voltage magnitude, enabling systematic evaluation of classical, fully quantum, and hybrid quantum-classical models. The study innovatively integrates quantum feature maps with a radial basis function support vector machine (RBF-SVM), achieving more stable training and higher detection accuracy under the constraints of current noisy intermediate-scale quantum (NISQ) hardware. Experimental results demonstrate that the proposed hybrid model slightly outperforms a strong classical RBF-SVM baseline in both accuracy and F1 score, whereas the fully quantum model exhibits inferior performance due to training instability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems because they modify control and measurement signals while remaining close to normal behavior, making them difficult to detect using standard intrusion detection methods. This study investigates quantum machine learning approaches for detecting coordinated stealth attacks on a distributed generation unit in a microgrid. High-quality simulated measurements were used to create a balanced binary classification dataset using three features: reactive power at DG1, frequency deviation relative to the nominal value, and terminal voltage magnitude. Classical machine learning baselines, fully quantum variational classifiers, and hybrid quantum classical models were evaluated. The results show that a hybrid quantum classical model combining quantum feature embeddings with a classical RBF support vector machine achieves the best overall performance on this low dimensional dataset, with a modest improvement in accuracy and F1 score over a strong classical SVM baseline. Fully quantum models perform worse due to training instability and limitations of current NISQ hardware. In contrast, hybrid models train more reliably and demonstrate that quantum feature mapping can enhance intrusion detection even when fully quantum learning is not yet practical.

Problem

• coordinated stealth attacks
• distributed generation systems
• cybersecurity
• intrusion detection
• quantum machine learning

Innovation

• quantum machine learning
• hybrid quantum-classical model
• coordinated stealth attack detection
• quantum feature embedding
• distributed generation systems