Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Selamnia, A.; Moudoud, H.; Khoukhi, L.; Brik, B.; Houda, Z. A. El
QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks Article d'actes
Dans: IEEE Int Conf Commun, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 15503607 (ISSN); 979-831954209-0 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Résumé | Liens | BibTeX | Étiquettes: 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
@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}
}
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.
2.
Chagnon-Forget, M.; Rouhafzay, G.; Cretu, A. -M.; Bouchard, S.
Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments Article de journal
Dans: 3D Research, vol. 7, no 4, 2016, ISSN: 20926731 (ISSN), (Publisher: 3D Display Research Center).
Résumé | Liens | BibTeX | Étiquettes: 3-d modeling, 3D modeling, Behavioral research, Complex networks, Interest points, Level of detail, Mesh simplification, Mesh simplifications, Saliency, Three dimensional computer graphics, virtual reality, Visual Attention
@article{chagnon-forget_enhanced_2016,
title = {Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments},
author = {M. Chagnon-Forget and G. Rouhafzay and A. -M. Cretu and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991727567&doi=10.1007%2fs13319-016-0106-7&partnerID=40&md5=136feba48a1aa3ff798619f8d734c956},
doi = {10.1007/s13319-016-0106-7},
issn = {20926731 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {3D Research},
volume = {7},
number = {4},
publisher = {3D Display Research Center},
abstract = {Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object. © 2016, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.},
note = {Publisher: 3D Display Research Center},
keywords = {3-d modeling, 3D modeling, Behavioral research, Complex networks, Interest points, Level of detail, Mesh simplification, Mesh simplifications, Saliency, Three dimensional computer graphics, virtual reality, Visual Attention},
pubstate = {published},
tppubtype = {article}
}
Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object. © 2016, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.
3.
Chagnon-Forget, M.; Rouhafzay, G.; Cretu, A. -M.; Bouchard, S.
Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments Article de journal
Dans: 3D Research, vol. 7, no 4, 2016, ISSN: 20926731 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: 3-d modeling, 3D modeling, Behavioral research, Complex networks, Interest points, Level of detail, Mesh simplification, Mesh simplifications, Saliency, Three dimensional computer graphics, virtual reality, Visual Attention
@article{chagnon-forgetEnhancedVisualAttentionModel2016,
title = {Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments},
author = {M. Chagnon-Forget and G. Rouhafzay and A. -M. Cretu and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991727567&doi=10.1007%2fs13319-016-0106-7&partnerID=40&md5=136feba48a1aa3ff798619f8d734c956},
doi = {10.1007/s13319-016-0106-7},
issn = {20926731 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {3D Research},
volume = {7},
number = {4},
publisher = {3D Display Research Center},
abstract = {Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object. © 2016, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.},
keywords = {3-d modeling, 3D modeling, Behavioral research, Complex networks, Interest points, Level of detail, Mesh simplification, Mesh simplifications, Saliency, Three dimensional computer graphics, virtual reality, Visual Attention},
pubstate = {published},
tppubtype = {article}
}
Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object. © 2016, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.