Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems
This addresses cybersecurity threats in microgrids, but the results are incremental due to limited dataset and hardware constraints.
The study tackled detecting coordinated stealth attacks in distributed generation systems by evaluating quantum machine learning approaches, finding that a hybrid quantum-classical model achieved modest improvements in accuracy and F1 score over classical baselines on a low-dimensional dataset.
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.