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. A. El; Moudoud, H.; Brik, B.; Khoukhi, L.
Securing O-RAN Equipment Using Blockchain-Based Supply Chain Verification Article d'actes
Dans: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC, p. 1570–1575, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833150887-6 (ISBN), (Journal Abbreviation: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC).
Résumé | Liens | BibTeX | Étiquettes: Access network equipment, Authentication, Block-chain, Blockchain, Cryptography, Denial-of-service attack, Firmware, Firmware authentication, Multi-vendor, Network architecture, Network security, O-RAN, Open radio access network, Radio access networks, Security, Security systems, Security vulnerabilities, Supply Chain Verification, Supply chains, Telecommunications networks
@inproceedings{mehrbanSecuringORANEquipment2025,
title = {Securing O-RAN Equipment Using Blockchain-Based Supply Chain Verification},
author = {A. Mehrban and Z. A. El Houda and H. Moudoud and B. Brik and L. Khoukhi},
url = {https://www.scopus.com/pages/publications/105011364438?origin=resultslist},
doi = {10.1109/IWCMC65282.2025.11059692},
isbn = {979-833150887-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int. Wirel. Commun. Mob. Comput. Conf., IWCMC},
pages = {1570–1575},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The Open Radio Access Network (O-RAN) architecture has enabled the integration of multi-vendor equipment, yielding a significant enhancement in the flexibility and interoperability of telecommunications networks. However, this openness has also introduced new security vulnerabilities, particularly in supply chain integrity. Malicious actors may exploit weaknesses at various stages of production, distribution, or integration, leading to critical threats such as data tampering, unauthorized access, and denial-of-service (DOS) attacks. To address these challenges, this paper proposes a novel blockchain-based framework designed to secure the O-RAN supply chain. The proposed solution leverages a private permissioned blockchain ledger and cryptographic firmware authentication to ensure the integrity and authenticity of network equipment throughout its lifecycle. Specifically, the framework consists of: (1) a decentralized architecture integrating blockchain network components, equipment node validators, and secure firmware authentication mechanisms; and (2) a consensus-based verification model to enhance trust and transparency within the supply chain. To the best of our knowledge, this is one of the first approaches to use blockchain for O-RAN supply chain security, and also addressing emerging security threats in a scalable and tamper-resistant manner. Experimental validation and security assessments demonstrate the effectiveness of the proposed framework in mitigating supply chain risks, making it a promising solution for ensuring trust and robustness in next-generation O-RAN ecosystems. © 2025 IEEE.},
note = {Journal Abbreviation: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC},
keywords = {Access network equipment, Authentication, Block-chain, Blockchain, Cryptography, Denial-of-service attack, Firmware, Firmware authentication, Multi-vendor, Network architecture, Network security, O-RAN, Open radio access network, Radio access networks, Security, Security systems, Security vulnerabilities, Supply Chain Verification, Supply chains, Telecommunications networks},
pubstate = {published},
tppubtype = {inproceedings}
}
The Open Radio Access Network (O-RAN) architecture has enabled the integration of multi-vendor equipment, yielding a significant enhancement in the flexibility and interoperability of telecommunications networks. However, this openness has also introduced new security vulnerabilities, particularly in supply chain integrity. Malicious actors may exploit weaknesses at various stages of production, distribution, or integration, leading to critical threats such as data tampering, unauthorized access, and denial-of-service (DOS) attacks. To address these challenges, this paper proposes a novel blockchain-based framework designed to secure the O-RAN supply chain. The proposed solution leverages a private permissioned blockchain ledger and cryptographic firmware authentication to ensure the integrity and authenticity of network equipment throughout its lifecycle. Specifically, the framework consists of: (1) a decentralized architecture integrating blockchain network components, equipment node validators, and secure firmware authentication mechanisms; and (2) a consensus-based verification model to enhance trust and transparency within the supply chain. To the best of our knowledge, this is one of the first approaches to use blockchain for O-RAN supply chain security, and also addressing emerging security threats in a scalable and tamper-resistant manner. Experimental validation and security assessments demonstrate the effectiveness of the proposed framework in mitigating supply chain risks, making it a promising solution for ensuring trust and robustness in next-generation O-RAN ecosystems. © 2025 IEEE.
2.
Moudoud, H.; Houda, Z. A. El; Brik, B.
Securing O-RAN with Zero Trust Architecture and Large Language Models Article d'actes
Dans: C., Iwendi; Z., Boulouard; N., Kryvinska (Ed.): Lect. Notes Networks Syst., p. 357–368, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23673370 (ISSN); 978-303194619-6 (ISBN), (Journal Abbreviation: Lect. Notes Networks Syst.).
Résumé | Liens | BibTeX | Étiquettes: Access management, Access Management system, Architecture, Authentication, Block-chain, Blockchain, Computer architecture, Computer crime, Cryptography, Distributed computer systems, Intrusion Detection, Language model, Large language model, Management systems, Mobile security, Mobile telecommunication systems, Network architecture, Network security, O-RAN, Open radio access network, Radio access networks, Security systems, Security vulnerabilities, Trusted computing, Zero Trust
@inproceedings{moudoudSecuringORANZero2025,
title = {Securing O-RAN with Zero Trust Architecture and Large Language Models},
author = {H. Moudoud and Z. A. El Houda and B. Brik},
editor = {Iwendi C. and Boulouard Z. and Kryvinska N.},
url = {https://www.scopus.com/pages/publications/105011259647?origin=resultslist},
doi = {10.1007/978-3-031-94620-2_31},
isbn = {23673370 (ISSN); 978-303194619-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Networks Syst.},
volume = {1312 LNNS},
pages = {357–368},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The Open Radio Access Network (O-RAN) architecture is critical for the development of 6G networks, offering flexibility and interoperability through disaggregated components. However, this openness exposes O-RAN to new security vulnerabilities, including unauthorized access, data breaches, and malicious xApp deployments. To address these challenges, we propose DistillORAN, a novel Zero-Trust architecture designed specifically for O-RAN. DistillORAN features two core components: (1) a blockchain-based decentralized trust management system for secure verification, authentication, and dynamic access control of xApps, and (2) a lightweight intrusion detection module powered by DistilBERT, a transformer-based model optimized for resource-constrained environments. DistilBERT’s ability to analyze network activities and detect anomalies in real-time allows it to identify complex security threats and multi-step attack scenarios within the O-RAN ecosystem. Its lightweight nature makes it ideal for O-RAN’s distributed infrastructure, where computational resources may be limited. By combining blockchain technology for trust management with DistilBERT’s powerful pattern recognition for intrusion detection, DistillORAN enforces a Zero-Trust security model, ensuring continuous monitoring and verification of all network components. This comprehensive solution enhances the security and resilience of O-RAN networks, aligning with the dynamic needs of next-generation mobile infrastructures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Networks Syst.},
keywords = {Access management, Access Management system, Architecture, Authentication, Block-chain, Blockchain, Computer architecture, Computer crime, Cryptography, Distributed computer systems, Intrusion Detection, Language model, Large language model, Management systems, Mobile security, Mobile telecommunication systems, Network architecture, Network security, O-RAN, Open radio access network, Radio access networks, Security systems, Security vulnerabilities, Trusted computing, Zero Trust},
pubstate = {published},
tppubtype = {inproceedings}
}
The Open Radio Access Network (O-RAN) architecture is critical for the development of 6G networks, offering flexibility and interoperability through disaggregated components. However, this openness exposes O-RAN to new security vulnerabilities, including unauthorized access, data breaches, and malicious xApp deployments. To address these challenges, we propose DistillORAN, a novel Zero-Trust architecture designed specifically for O-RAN. DistillORAN features two core components: (1) a blockchain-based decentralized trust management system for secure verification, authentication, and dynamic access control of xApps, and (2) a lightweight intrusion detection module powered by DistilBERT, a transformer-based model optimized for resource-constrained environments. DistilBERT’s ability to analyze network activities and detect anomalies in real-time allows it to identify complex security threats and multi-step attack scenarios within the O-RAN ecosystem. Its lightweight nature makes it ideal for O-RAN’s distributed infrastructure, where computational resources may be limited. By combining blockchain technology for trust management with DistilBERT’s powerful pattern recognition for intrusion detection, DistillORAN enforces a Zero-Trust security model, ensuring continuous monitoring and verification of all network components. This comprehensive solution enhances the security and resilience of O-RAN networks, aligning with the dynamic needs of next-generation mobile infrastructures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
3.
Soultana, O. A.; Moudoud, H.
Adaptive Heterogeneous Ensemble Learning for Attack Detection in IoT Networks Article d'actes
Dans: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., p. 27–32, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Résumé | Liens | BibTeX | Étiquettes: Attack detection, Classification (of information), Computational efficiency, Detection accuracy, Ensemble learning, Ensemble techniques, Heterogeneous ensembles, Internet of thing security, Internet of things, Intrusion Detection, Intrusion-Detection, IoT Security, Learning systems, Nearest neighbor search, Security vulnerabilities, Stackings, Support vector machines, Zero-day attack, Zero-day detection
@inproceedings{soultanaAdaptiveHeterogeneousEnsemble2025,
title = {Adaptive Heterogeneous Ensemble Learning for Attack Detection in IoT Networks},
author = {O. A. Soultana and H. Moudoud},
url = {https://www.scopus.com/pages/publications/105033149093?origin=resultslist},
doi = {10.1109/SMC58881.2025.11343130},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {27–32},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The proliferation of Internet of Things (IoT) devices has introduced significant security vulnerabilities, particularly in detecting zero-day attacks within highly dynamic and heterogeneous environments. Traditional machine learning models often fall short due to their static nature and computational demands. In this paper, we propose an adaptive ensemble learning framework that dynamically selects optimal detection models on a per-attack-class basis to improve detection accuracy while maintaining computational efficiency. Our approach combines multiple base classifiers (Random Forest, K-Nearest Neighbors, and Support Vector Machine) using ensemble techniques including bagging, boosting, and stacking. Ensemble techniques such as Bagging, Boosting, Voting, and Stacking. The key innovation lies in a class-aware model selection mechanism that identifies the most effective classifier-ensemble combination for each specific attack category, rather than applying a single model across all threat types. This targeted approach recognizes that different attack patterns exhibit distinct characteristics that may be better captured by different algorithmic approaches. Finally, we propose a decision-rule mechanism that selects the best-performing model for each attack class to improve detection accuracy. The proposed framework is evaluated through extensive experiments. The results show that our approach significantly enhances classification performance, especially for complex and rare attack types. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {Attack detection, Classification (of information), Computational efficiency, Detection accuracy, Ensemble learning, Ensemble techniques, Heterogeneous ensembles, Internet of thing security, Internet of things, Intrusion Detection, Intrusion-Detection, IoT Security, Learning systems, Nearest neighbor search, Security vulnerabilities, Stackings, Support vector machines, Zero-day attack, Zero-day detection},
pubstate = {published},
tppubtype = {inproceedings}
}
The proliferation of Internet of Things (IoT) devices has introduced significant security vulnerabilities, particularly in detecting zero-day attacks within highly dynamic and heterogeneous environments. Traditional machine learning models often fall short due to their static nature and computational demands. In this paper, we propose an adaptive ensemble learning framework that dynamically selects optimal detection models on a per-attack-class basis to improve detection accuracy while maintaining computational efficiency. Our approach combines multiple base classifiers (Random Forest, K-Nearest Neighbors, and Support Vector Machine) using ensemble techniques including bagging, boosting, and stacking. Ensemble techniques such as Bagging, Boosting, Voting, and Stacking. The key innovation lies in a class-aware model selection mechanism that identifies the most effective classifier-ensemble combination for each specific attack category, rather than applying a single model across all threat types. This targeted approach recognizes that different attack patterns exhibit distinct characteristics that may be better captured by different algorithmic approaches. Finally, we propose a decision-rule mechanism that selects the best-performing model for each attack class to improve detection accuracy. The proposed framework is evaluated through extensive experiments. The results show that our approach significantly enhances classification performance, especially for complex and rare attack types. © 2025 IEEE.