@inproceedings{selamniaQSFLIDQuantumSplitFederated2026,

title = {QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks},

author = {A. Selamnia and H. Moudoud and L. Khoukhi and B. Brik and Z. A. El Houda},

url = {https://www.scopus.com/pages/publications/105045419288?origin=resultslist},

doi = {10.1109/ICC59461.2026.11587037},

isbn = {15503607 (ISSN); 979-831954209-0 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {IEEE Int Conf Commun},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The Industrial Internet of Things (IIoT) is reshaping industrial automation through interconnected, intelligent systems. However, this evolution increases exposure to sophisticated cyber threats, especially given the constraints of IIoT devices such as limited processing power, bandwidth, and heterogeneous protocols. Traditional machine learning methods often fail to meet these security demands due to their computational intensity and centralized data requirements. To address this, we propose a hybrid quantum-classical Split Federated Learning (SFL) framework for intrusion detection in IIoT networks. Our method integrates Variational Quantum Circuits (VQCs) to model complex, non-linear data relationships, enhancing detection accuracy while preserving data privacy through decentralized learning. The architecture assigns lightweight preprocessing to edge devices and complex analysis to a quantum backend, ensuring efficiency and scalability. To evaluate the proposed framework, we conduct extensive experiments on the real-world EDGE-IIoT dataset; the experimental results demonstrate that the model attains 95.5% training accuracy, significantly surpassing classical SFL (85.7%). In addition, the quantum model's enhanced entanglement properties and expressibility strengthen its generalization performance. This approach offers an efficient and privacy-preserving solution for securing IIoT systems. © 2026 IEEE.},

note = {Journal Abbreviation: IEEE Int Conf Commun},

keywords = {Automation, Complex networks, Cyber threats, Federated learning, IIoT, Industrial automation, Industrial internet of thing, Intrusion Detection, Intrusion-Detection, Learning systems, Machine learning methods, Network intrusion, Network security, Privacy-preserving techniques, Processing power, QML, Quantum circuit, Quantum entanglement, Split Learning, Variational quantum circuit, VQC},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{merzoukiGraphNeuralNetwork2025,

title = {Graph Neural Network Framework for Advanced Persistent Threat Detection in IIoT Environments},

author = {K. Merzouki and Y. Hadi and H. Elghazi and H. Moudoud and Z. A. El Houda},

editor = {El Bouanani F. and Ayoub F.},

url = {https://www.scopus.com/pages/publications/105032074689?origin=resultslist},

doi = {10.1109/CommNet68224.2025.11288886},

isbn = {979-833155781-2 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Conf. Adv. Commun. Technol. Netw., CommNet - Proc.},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The increasing reliance of industrial infrastructures on the Industrial Internet of Things (IIoT) has made them more vulnerable to complex cyberattacks, especially Advanced Persistent Threats (APTs). To recognize and analyze these multi-stage incursions, we require computational models that can capture both structural and temporal connections in IIoT network architectures. This paper presents a framework based on Graph Neural Networks (GNNs) for detecting and classifying APTs in IIoT settings. We use the CICAPT-IIoT2024 dataset, which provides realistic multi-phase APT attack scenarios. The approach models system components, network communications, and process interactions as nodes and edges in a dynamic graph, allowing for relational learning and context-aware feature extraction. The GNN architecture leverages graph connectivity patterns and message-passing techniques to identify attack phases with greater accuracy and robustness. Experimental results show that this method outperforms traditional deep learning techniques and ensemble methods, particularly in early-stage anomaly detection. This paper highlights the potential of graph-based learning as an effective way to enhance IIoT infrastructure security against the changing behaviors of advanced persistent threats. © 2025 IEEE.},

note = {Journal Abbreviation: Int. Conf. Adv. Commun. Technol. Netw., CommNet - Proc.},

keywords = {Advanced persistent threat, Advanced Persistent Threats, Anomaly detection, CICAPT-IIoT2024, Deep learning, extraction, Feature extraction, Features extraction, Graph Neural Networks, Graphic methods, IIoT, Industrial infrastructure, Industrial internet of thing, Learning systems, Message passing, Network architecture, Network frameworks, Network security, Relational learning, Threat detection},

pubstate = {published},

tppubtype = {inproceedings}

}