Open Access

ARTICLE

Quantum-Enhanced Security for Edge-IIoT: Robust Intrusion Detection with a Novel Quantum-Classical Neural Network

Alanoud Al Mazroa¹, Abdulrahman Mohammed Alamoudi², Nurdaulet Karabayev³, Jawad Ahmad^4,*, Muhammad Shahbaz Khan⁵

1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
2 Department of Electrical and Electronic Engineering, College of Engineering, University of Jeddah, Jeddah, Saudi Arabia
3 Faculty of Information Technology, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
4 Cybersecurity Center, Prince Mohammad Bin Fahd University, Al-Khobar, Saudi Arabia
5 School of Computer Science and Digital Technologies, Aston University, Birmingham, UK

* Corresponding Author: Jawad Ahmad. Email:

Computers, Materials & Continua 2026, 89(1), 76 https://doi.org/10.32604/cmc.2026.083381

Received 02 April 2026; Accepted 03 July 2026; Issue published 13 August 2026

Abstract

The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these challenges, this paper proposes a Residual Hybrid Quantum-Classical Neural Network (RHQ-CNN) for efficient intrusion detection in Edge-Industrial Internet of Things (Edge-IIoT) environments under idealized quantum simulation conditions. The proposed framework employs a classical neural network encoder to compress high-dimensional network traffic into a compact latent representation suitable for quantum processing. A variational quantum circuit with serial data re-uploading is then utilised to model complex non-linear decision boundaries without increasing qubit requirements. In addition, a residual connection fuses classical and quantum representations to improve training stability and preserve latent feature information. The model is evaluated on the Edge-IIoTset dataset and achieves a test accuracy of 99.94%, with high weighted performance across the 15-class detection task, although the extremely low-sample Fingerprinting class remains comparatively more challenging. Additional controlled ablation, deployment-cost, and bootstrap confidence interval analyses demonstrate the test-set metric stability and computational trade-offs of the proposed architecture. These findings highlight the potential of hybrid quantum-classical models for next-generation cybersecurity in industrial IoT systems.

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