Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Mehrban, A.; Houda, Z. Abou El; Moudoud, H.; Bao, L. Le
Integrating Zero Trust Architecture in O-RAN: A Comprehensive Survey and Analysis Journal Article
In: IEEE Open Journal of the Communications Society, vol. 6, pp. 10465–10495, 2025, ISSN: 2644125X (ISSN).
Abstract | Links | BibTeX | Tags: 6g network, 6G networks, Access control, Data flow analysis, Intelligent controllers, Interoperability, Mobile telecommunication systems, Multi-vendor, Network architecture, Network components, Network security, New approaches, Open radio access network, open radio access network (O-RAN), Radio access networks, RAN intelligent controller, RAN intelligent controller (RIC), Smart contract, Survey and analysis, Trusted computing, Vendor interoperability, Zero trust architecture, Zero trust architecture (ZTA)
@article{mehrbanIntegratingZeroTrust2025,
title = {Integrating Zero Trust Architecture in O-RAN: A Comprehensive Survey and Analysis},
author = {A. Mehrban and Z. Abou El Houda and H. Moudoud and L. Le Bao},
url = {https://www.scopus.com/pages/publications/105025432034?origin=resultslist},
doi = {10.1109/OJCOMS.2025.3644132},
issn = {2644125X (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Open Journal of the Communications Society},
volume = {6},
pages = {10465–10495},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Open Radio Access Network (O-RAN) is a new approach to mobile networks that disaggregates the network architecture into multi-vendor interoperable physical and software-defined network components connected through standardized open interfaces. This architecture enables deploying solutions on cloud-native platforms and boosting Artificial Intelligence(AI)-driven automation for network optimization. Despite the benefits of O-RAN’s heterogeneous and multi-vendor architecture, this approach inevitably enlarges the attack surface and introduces additional trust boundaries, which imminently threaten the uniformity of network performance. This also justifies the necessity of Zero Trust Architecture (ZTA) principles as a countermeasure, securing all network components, interfaces, and data flows. This survey shows how ZTA tenets can be integrated into O-RAN settings, contributing in three key areas: (1) a novel ZTA-to-O-RAN mapping model that explicitly places Policy Engine (PE), Policy Administrator (PA), and Policy Enforcement Points (PEPs) across RIC layers (Non-RT/Near-RT RIC), standardized interfaces (A1/E2/O1/O2), and disaggregated RAN functions (O-DU/O-CU/O-RU); (2) a Risk-Adaptive Access Control (RAdAC) framework that dynamically modulates verification depth based on contextual risk; and (3) integration of blockchain-based decentralized identity management with smart-contract-driven authorization for xApp/rApp lifecycle security. © 2020 IEEE.},
keywords = {6g network, 6G networks, Access control, Data flow analysis, Intelligent controllers, Interoperability, Mobile telecommunication systems, Multi-vendor, Network architecture, Network components, Network security, New approaches, Open radio access network, open radio access network (O-RAN), Radio access networks, RAN intelligent controller, RAN intelligent controller (RIC), Smart contract, Survey and analysis, Trusted computing, Vendor interoperability, Zero trust architecture, Zero trust architecture (ZTA)},
pubstate = {published},
tppubtype = {article}
}
Open Radio Access Network (O-RAN) is a new approach to mobile networks that disaggregates the network architecture into multi-vendor interoperable physical and software-defined network components connected through standardized open interfaces. This architecture enables deploying solutions on cloud-native platforms and boosting Artificial Intelligence(AI)-driven automation for network optimization. Despite the benefits of O-RAN’s heterogeneous and multi-vendor architecture, this approach inevitably enlarges the attack surface and introduces additional trust boundaries, which imminently threaten the uniformity of network performance. This also justifies the necessity of Zero Trust Architecture (ZTA) principles as a countermeasure, securing all network components, interfaces, and data flows. This survey shows how ZTA tenets can be integrated into O-RAN settings, contributing in three key areas: (1) a novel ZTA-to-O-RAN mapping model that explicitly places Policy Engine (PE), Policy Administrator (PA), and Policy Enforcement Points (PEPs) across RIC layers (Non-RT/Near-RT RIC), standardized interfaces (A1/E2/O1/O2), and disaggregated RAN functions (O-DU/O-CU/O-RU); (2) a Risk-Adaptive Access Control (RAdAC) framework that dynamically modulates verification depth based on contextual risk; and (3) integration of blockchain-based decentralized identity management with smart-contract-driven authorization for xApp/rApp lifecycle security. © 2020 IEEE.
2.
Boulmerka, A.; Allili, M. S.
Thresholding-based segmentation revisited using mixtures of generalized Gaussian distributions Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, pp. 2894–2897, Tsukuba, 2012, ISSN: 10514651, (ISSN: 10514651).
Abstract | Links | BibTeX | Tags: Arbitrary number, Gaussian noise (electronic), Generalized Gaussian Distributions, Heavy-tailed, Image segmentation, Kittler, Minimum error thresholding, Multi-modal, New approaches, Non-Gaussian, Otsu's method, Pattern Recognition, State-of-the-art techniques, Synthetic data
@inproceedings{boulmerka_thresholding-based_2012,
title = {Thresholding-based segmentation revisited using mixtures of generalized Gaussian distributions},
author = {A. Boulmerka and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874575463&partnerID=40&md5=0665cce9aa19af524d1213c1ff728d94},
issn = {10514651},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
pages = {2894–2897},
address = {Tsukuba},
abstract = {This paper presents a new approach to image-thresholding-based segmentation. It considerably improves existing methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions. The proposed approach seamlessly: 1) extends the Otsu's method to arbitrary numbers of thresholds and 2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. We use the recently-proposed mixture of generalized Gaussian distributions (MoGG) modeling, which enables to efficiently represent heavy-tailed data, as well as multi-modal histograms with flat and sharply-shaped peaks. Experiments performed on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques. © 2012 ICPR Org Committee.},
note = {ISSN: 10514651},
keywords = {Arbitrary number, Gaussian noise (electronic), Generalized Gaussian Distributions, Heavy-tailed, Image segmentation, Kittler, Minimum error thresholding, Multi-modal, New approaches, Non-Gaussian, Otsu's method, Pattern Recognition, State-of-the-art techniques, Synthetic data},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a new approach to image-thresholding-based segmentation. It considerably improves existing methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions. The proposed approach seamlessly: 1) extends the Otsu's method to arbitrary numbers of thresholds and 2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. We use the recently-proposed mixture of generalized Gaussian distributions (MoGG) modeling, which enables to efficiently represent heavy-tailed data, as well as multi-modal histograms with flat and sharply-shaped peaks. Experiments performed on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques. © 2012 ICPR Org Committee.
3.
Boulmerka, A.; Allili, M. S.
Thresholding-based segmentation revisited using mixtures of generalized Gaussian distributions Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, pp. 2894–2897, Tsukuba, 2012, ISSN: 10514651.
Abstract | Links | BibTeX | Tags: Arbitrary number, Gaussian noise (electronic), Generalized Gaussian Distributions, Heavy-tailed, Image segmentation, Kittler, Minimum error thresholding, Multi-modal, New approaches, Non-Gaussian, Otsu's method, Pattern Recognition, State-of-the-art techniques, Synthetic data
@inproceedings{boulmerkaThresholdingbasedSegmentationRevisited2012,
title = {Thresholding-based segmentation revisited using mixtures of generalized Gaussian distributions},
author = {A. Boulmerka and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874575463&partnerID=40&md5=0665cce9aa19af524d1213c1ff728d94},
issn = {10514651},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
pages = {2894–2897},
address = {Tsukuba},
abstract = {This paper presents a new approach to image-thresholding-based segmentation. It considerably improves existing methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions. The proposed approach seamlessly: 1) extends the Otsu's method to arbitrary numbers of thresholds and 2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. We use the recently-proposed mixture of generalized Gaussian distributions (MoGG) modeling, which enables to efficiently represent heavy-tailed data, as well as multi-modal histograms with flat and sharply-shaped peaks. Experiments performed on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques. © 2012 ICPR Org Committee.},
keywords = {Arbitrary number, Gaussian noise (electronic), Generalized Gaussian Distributions, Heavy-tailed, Image segmentation, Kittler, Minimum error thresholding, Multi-modal, New approaches, Non-Gaussian, Otsu's method, Pattern Recognition, State-of-the-art techniques, Synthetic data},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a new approach to image-thresholding-based segmentation. It considerably improves existing methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions. The proposed approach seamlessly: 1) extends the Otsu's method to arbitrary numbers of thresholds and 2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. We use the recently-proposed mixture of generalized Gaussian distributions (MoGG) modeling, which enables to efficiently represent heavy-tailed data, as well as multi-modal histograms with flat and sharply-shaped peaks. Experiments performed on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques. © 2012 ICPR Org Committee.
4.
Allili, M. S.; Ziou, D.
An approach for dynamic combination of region and boundary information in segmentation Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers Inc., 2008, ISBN: 10514651 (ISSN); 978-142442175-6 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.).
Abstract | Links | BibTeX | Tags: Arbitrary weighting, Bayesian formulation, Bayesian networks, Boundary information, Dynamic combination, Energy functionals, Hyper-parameter, Image segmentation, New approaches, Parameter estimation, Pattern Recognition, Region information
@inproceedings{allili_approach_2008,
title = {An approach for dynamic combination of region and boundary information in segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957954657&doi=10.1109%2ficpr.2008.4761384&partnerID=40&md5=92c6dc032da4938d5c1c3ced6af1671c},
doi = {10.1109/icpr.2008.4761384},
isbn = {10514651 (ISSN); 978-142442175-6 (ISBN)},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Image segmentation combining boundary and region information has been the subject of numerous research works in the past. This combination is usually subject to arbitrary weighting parameters (hyper-parameters) that control the contribution of boundary and region features during segmentation. In this work, we investigate a new approach for estimating the hyper-parameters adaptively to segmentation. The approach takes its roots from the physical properties of the energy functional controlling segmentation and a Bayesian formulation of segmentation and hyper-parameters estimation. © 2008 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.},
keywords = {Arbitrary weighting, Bayesian formulation, Bayesian networks, Boundary information, Dynamic combination, Energy functionals, Hyper-parameter, Image segmentation, New approaches, Parameter estimation, Pattern Recognition, Region information},
pubstate = {published},
tppubtype = {inproceedings}
}
Image segmentation combining boundary and region information has been the subject of numerous research works in the past. This combination is usually subject to arbitrary weighting parameters (hyper-parameters) that control the contribution of boundary and region features during segmentation. In this work, we investigate a new approach for estimating the hyper-parameters adaptively to segmentation. The approach takes its roots from the physical properties of the energy functional controlling segmentation and a Bayesian formulation of segmentation and hyper-parameters estimation. © 2008 IEEE.
5.
Allili, M. S.; Ziou, D.
An approach for dynamic combination of region and boundary information in segmentation Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers Inc., 2008, ISBN: 10514651 (ISSN); 978-142442175-6 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.).
Abstract | Links | BibTeX | Tags: Arbitrary weighting, Bayesian formulation, Bayesian networks, Boundary information, Dynamic combination, Energy functionals, Hyper-parameter, Image segmentation, New approaches, Parameter estimation, Pattern Recognition, Region information
@inproceedings{alliliApproachDynamicCombination2008,
title = {An approach for dynamic combination of region and boundary information in segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957954657&doi=10.1109%2ficpr.2008.4761384&partnerID=40&md5=92c6dc032da4938d5c1c3ced6af1671c},
doi = {10.1109/icpr.2008.4761384},
isbn = {10514651 (ISSN); 978-142442175-6 (ISBN)},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Image segmentation combining boundary and region information has been the subject of numerous research works in the past. This combination is usually subject to arbitrary weighting parameters (hyper-parameters) that control the contribution of boundary and region features during segmentation. In this work, we investigate a new approach for estimating the hyper-parameters adaptively to segmentation. The approach takes its roots from the physical properties of the energy functional controlling segmentation and a Bayesian formulation of segmentation and hyper-parameters estimation. © 2008 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.},
keywords = {Arbitrary weighting, Bayesian formulation, Bayesian networks, Boundary information, Dynamic combination, Energy functionals, Hyper-parameter, Image segmentation, New approaches, Parameter estimation, Pattern Recognition, Region information},
pubstate = {published},
tppubtype = {inproceedings}
}
Image segmentation combining boundary and region information has been the subject of numerous research works in the past. This combination is usually subject to arbitrary weighting parameters (hyper-parameters) that control the contribution of boundary and region features during segmentation. In this work, we investigate a new approach for estimating the hyper-parameters adaptively to segmentation. The approach takes its roots from the physical properties of the energy functional controlling segmentation and a Bayesian formulation of segmentation and hyper-parameters estimation. © 2008 IEEE.