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QKD-Secured Industrial Smart-Grid Cyber-Physical Systems: Simulation and Q-MambaKAN Detection of Adaptive Side-Channel Attacks

Ayoub Alsarhan, Bashar Khassawneh, Laith Alzboon, Kholoud Alkayid, Mahmoud AlJamal, Eslam Al Maghayreh, Fiyad Ahmad Alenazi, Hussein Ahmad Al-Ofeishat

Published Sep 3, 2026• 0 citations
Publisher

Abstract

The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021.

DOI
10.3390/fi18090468

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The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021.
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Smart grid solutions enable utilities and customers to better monitor and control energy use via information and communications technology. Information technology is intended to improve the future electric grid's reliability, efficiency, and sustainability by implementing advanced monitoring and control systems. However, leveraging modern communications systems also makes the grid vulnerable to cyberattacks. Here we report the first use of quantum key distribution (QKD) keys in the authentication of smart grid communications. In particular, we make such demonstration on a deployed electric utility fiber network. The developed method was prototyped in a software package to manage and utilize cryptographic keys to authenticate machine-to-machine communications used for supervisory control and data acquisition (SCADA). This demonstration showcases the feasibility of using QKD to improve the security of critical infrastructure, including future distributed energy resources (DERs), such as energy storage.
The modernization of distributed energy systems introduces complex cybersecurity challenges as grid infrastructures become increasingly digitized, decentralized, and web-connected. This paper presents a novel security architecture that integrates quantum key distribution (QKD) with semantic web technologies to provide end-to-end secure, scalable, and adaptive protection for distributed energy resource (DER) networks. The proposed framework features a modular system design, incorporating BB84-based QKD protocols for quantum-resilient key generation, a metadata-driven policy layer using OWL ontologies and SWRL reasoning, and a web interface for operator access and real-time monitoring. Extensive performance evaluation in a simulated multi-domain microgrid environment demonstrates that the system achieves an average key generation rate of 2.3 kbps with quantum bit error rate (QBER) maintained below 5.2% across 40 km optical links. Session establishment latency averaged 435 ms, 29.8% lower than a traditional TLS/PKI baseline, while semantic access validation achieved 100% accuracy in 42 adversarial test cases. These cases were evaluated using automated semantic validation scripts simulating spoofed roles, malformed sessions, and unauthorized requests. The system sustained encrypted throughput of 110 messages per second per node and maintained service continuity under quantum noise and cross-domain attack simulations. Usability trials with six engineers yielded a system usability scale (SUS) score of 88.3, and the average DER onboarding time was reduced from 10.1 to 5.5 minutes. These findings affirm that QKD-enhanced, semantically governed web architectures can provide strong cryptographic guarantees while supporting dynamic policy enforcement and intuitive user workflows. The proposed solution demonstrates a viable path for deploying future-proof security mechanisms in next-generation smart grid environments.
Information and Communication Technology (ICT) provides customers with utilities and smart grid solutions, enabling enhanced monitoring and control of energy management systems. This technology is poised to elevate the reliability, sustainability, and efficiency of future electric grids through the implementation of advanced metering infrastructure (AMI). However, current Supervisory Control and Data Acquisition (SCADA) systems lack trusted machine authentication in smart grid communications, leaving the electric grid vulnerable to cyberattacks via sophisticated network technologies such as wireless access points, sensors, routers, and gateways. Therefore, ensuring proper management of data integrity from field sensors is crucial to enhance the reliability of SCADA systems. In this context, the utilization of quantum key distribution (QKD) key pairs is proposed to uphold integrity in smart grid communications. This paper presents a fibre optic blockchain network designed to manage and utilize cryptographic keys, facilitating the authentication of peer-to-peer (P2P) communications in SCADA systems. This demonstration underscores the feasibility of employing QKD and blockchain to further strengthen the integrity and authentication of smart grid communications. Additionally, this paper delves into discussing the performance metrics and overhead expenses of the proposed scheme in comparison with existing state-of-the-art proposals. Simulation results highlight the significant impact of blockchain size on the system setup’s throughput and latency.
The fast integration of information technology with operational technology in cyber-physical systems (CPS) has introduced new cybersecurity risks to critical infrastructure systems including power grids. Current cryptographic systems fail to protect data because emerging quantum computing technology will break existing standard encryption methods. Quantum Key Distribution (QKD) employs principles of quantum physics to provide secure communication through an unbreakable method. This paper examines the implementation of QKD technology for CPS systems focusing on SCADA systems of the power grid infrastructure. The paper delivers an extensive evaluation of the QKD system architecture together with feature extraction and classification approaches and practical system implementations. The analysis focuses on hydroelectric systems and wide-area SCADA deployments and post-quantum cryptographic resilience. An extensive review of the existing literature helps us understand the current state, while providing essential knowledge gaps before suggesting research pathways for quantum-enhanced CPS security.
Quantum key distribution (QKD) is widely regarded as a highly secure quantum encryption approach since it applies theoretical principles of quantum mechanics as a medium of security, unlike classical encryption processes that rely on complex mathematics. However, practical QKD systems are vulnerable due to imperfect photon source hardware, communication channel, and detector implementation. These hardware imperfections open side channels that an adversary can exploit without violating principles of quantum mechanics. This review provides a comprehensive overview of the current QKD protocols, quantum hacking strategies and their operating principles, vulnerable hardware components and their mitigation techniques. This paper further investigates how state-of-the-art paradigms based on artificial intelligence (AI) and machine learning (ML), along with key subfields—such as deep learning (DL), deep reinforcement learning (DRL), and quantum machine learning (QML)—are reshaping the domain. In particular, this paper analyzes their dual roles as enablers of sophisticated attacks and as instruments for attack detection and mitigation. By jointly addressing the challenges in quantum communication and adaptive learning, as well as attack and detection architectures, this survey elucidates both the capabilities and the limitations of contemporary attack-detection mechanisms in the field. This survey seeks to establish a unified framework that conceptually integrates physical-layer quantum hacking with AI-/ML-based security, emphasizing their dual function as both an intelligent defensive layer and a potential tool for advanced threats.