Exploring Advanced Quantum Ensemble for Industrial Control Systems Security
Year: 2025
Type: conference-paper
Abstract: The escalating complexity and sophistication of cyber threats targeting Industrial Control Systems (ICS) demand innovative solutions to ensure robust security. Quantum computing, with its unprecedented computational capabilities, emerges as a promising technology in this domain. In this paper, we present an advanced stacking ensemble framework that integrates three quantum classifiers: the Quantum Variational Classifier (QVC), the Quantum Support Vector Classifier (QSVC), and the Quantum Neural Network (QNN). Our ensemble approach optimizes qubit efficiency while delivering superior classification performance. We evaluated our approach on a real-time, imbalanced ICS power system dataset comprising 78, 345 samples representing natural and attack scenarios. The results demonstrate that our advanced ensemble framework outperforms baseline quantum classifiers and achieves these results with minimal qubit usage. This demonstrates our approach's practical viability in enhancing cybersecurity in ICS through quantum computing.
Authors: Danish Vasan, Mohammad Shameem, Mohammad Hammoudeh, Mussadiq Abdul Rahim, Hamad Naeem, Adel F. Ahmed
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413268024