Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Houda, Z. A. El; Moudoud, H.
Toward a Secure Zero-Touch Tactile Internet: Challenges and Opportunities Article de journal
Dans: IEEE Communications Magazine, vol. 64, no 1, p. 56–62, 2026, ISSN: 01636804 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Communication service, Communications networks, High availability, High reliability, Low latency, Network security, Real-time application, Remote surgery, Security aspects, Security challenges, Tactile sensors, Ultra-high, virtual reality
@article{elhoudaSecureZeroTouchTactile2026,
title = {Toward a Secure Zero-Touch Tactile Internet: Challenges and Opportunities},
author = {Z. A. El Houda and H. Moudoud},
url = {https://www.scopus.com/pages/publications/105025652094?origin=resultslist},
doi = {10.1109/MCOM.001.2500259},
issn = {01636804 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Communications Magazine},
volume = {64},
number = {1},
pages = {56–62},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Tactile Internet (TI) is a new generation of communication networks, extending beyond 5G/6G capabilities to achieve ultra-low latency, ultra-responsive, ultra-high availability, and ultra-high reliability communication services and thus enabling a new era of real-time applications, such as virtual reality, remote surgery, and autonomous driving. TI is expected to revolutionize many industries, including healthcare, transportation, and manufacturing. Despite recent TI initiatives, security aspects remain largely unexplored. TI presents several security challenges due to the increased complexity of the network and the criticality of the applications it supports. Thus, security aspects must be carefully designed to ensure that the benefits of the TI are not outweighed by the risks. In this article, we will initially identify the leading factors that drive TI, with regard to their applications and related technological trends, with respect to ETSI's Zero-touch Service Management (ZSM) principles. Then, we shed light on the primary security challenges, the current missing building blocks toward secure TI, and propose some potential solutions. Finally, we conclude the article with several recommendations and observations for the roadmap toward secure TI. Ultimately, the focus of this article is to establish a foundation for more in-depth research on TI security. © 1979-2012 IEEE.},
keywords = {Communication service, Communications networks, High availability, High reliability, Low latency, Network security, Real-time application, Remote surgery, Security aspects, Security challenges, Tactile sensors, Ultra-high, virtual reality},
pubstate = {published},
tppubtype = {article}
}
Tactile Internet (TI) is a new generation of communication networks, extending beyond 5G/6G capabilities to achieve ultra-low latency, ultra-responsive, ultra-high availability, and ultra-high reliability communication services and thus enabling a new era of real-time applications, such as virtual reality, remote surgery, and autonomous driving. TI is expected to revolutionize many industries, including healthcare, transportation, and manufacturing. Despite recent TI initiatives, security aspects remain largely unexplored. TI presents several security challenges due to the increased complexity of the network and the criticality of the applications it supports. Thus, security aspects must be carefully designed to ensure that the benefits of the TI are not outweighed by the risks. In this article, we will initially identify the leading factors that drive TI, with regard to their applications and related technological trends, with respect to ETSI's Zero-touch Service Management (ZSM) principles. Then, we shed light on the primary security challenges, the current missing building blocks toward secure TI, and propose some potential solutions. Finally, we conclude the article with several recommendations and observations for the roadmap toward secure TI. Ultimately, the focus of this article is to establish a foundation for more in-depth research on TI security. © 1979-2012 IEEE.
2.
Zoungrana, A. F.; Moudoud, H.; Tajeuna, E. G.; Adi, K.
Adversarial Ensemble Framework: Leveraging GANs for Robust Intrusion Detection in IoT Networks Article d'actes
Dans: K., Adi; O., Nguena Timo; N., Boulahia-Cuppens; D., Espes; N., Stakhanova; M., Omar (Ed.): Lect. Notes Comput. Sci., p. 85–99, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303220731-9 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Adversarial networks, Class imbalance, Computer crime, Concept drifts, Gallium nitride, Generative adversarial networks, Internet of thing network, Internet of things, Intrusion Detection, Intrusion Detection Systems, Intrusion-Detection, IoT Networks, Key Issues, Network intrusion, Network security, Rapid expansion, Security, Security challenges
@inproceedings{zoungranaAdversarialEnsembleFramework2026,
title = {Adversarial Ensemble Framework: Leveraging GANs for Robust Intrusion Detection in IoT Networks},
author = {A. F. Zoungrana and H. Moudoud and E. G. Tajeuna and K. Adi},
editor = {Adi K. and Nguena Timo O. and Boulahia-Cuppens N. and Espes D. and Stakhanova N. and Omar M.},
url = {https://www.scopus.com/pages/publications/105046136116?origin=resultslist},
doi = {10.1007/978-3-032-20732-6_6},
isbn = {03029743 (ISSN); 978-303220731-9 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16295 LNCS},
pages = {85–99},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The rapid expansion of Internet of Things (IoT) devices introduces complex security challenges that traditional intrusion detection systems struggle to address. This paper proposes an Adversarial Ensemble Framework using Generative Adversarial Networks (GANs) to improve the accuracy and resilience of intrusion detection in IoT environments. The framework tackles key issues such as class imbalance, concept drift, and adversarial attacks by employing multiple GAN variants such as Vanilla GAN, Conditional GAN (CGAN), and Wasserstein GAN (WGAN) to generate high-quality synthetic attack data. A dynamic ensemble learning mechanism selects the most effective model for each attack type based on performance metrics. Experiments on NSL-KDD and CIC-IDS2017 show that WGAN yields the most effective synthetic data, contributing to a detection rate of up to 96%. The approach proves particularly effective in identifying rare attacks, making it a scalable and adaptive solution for IoT security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial networks, Class imbalance, Computer crime, Concept drifts, Gallium nitride, Generative adversarial networks, Internet of thing network, Internet of things, Intrusion Detection, Intrusion Detection Systems, Intrusion-Detection, IoT Networks, Key Issues, Network intrusion, Network security, Rapid expansion, Security, Security challenges},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid expansion of Internet of Things (IoT) devices introduces complex security challenges that traditional intrusion detection systems struggle to address. This paper proposes an Adversarial Ensemble Framework using Generative Adversarial Networks (GANs) to improve the accuracy and resilience of intrusion detection in IoT environments. The framework tackles key issues such as class imbalance, concept drift, and adversarial attacks by employing multiple GAN variants such as Vanilla GAN, Conditional GAN (CGAN), and Wasserstein GAN (WGAN) to generate high-quality synthetic attack data. A dynamic ensemble learning mechanism selects the most effective model for each attack type based on performance metrics. Experiments on NSL-KDD and CIC-IDS2017 show that WGAN yields the most effective synthetic data, contributing to a detection rate of up to 96%. The approach proves particularly effective in identifying rare attacks, making it a scalable and adaptive solution for IoT security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
3.
Houda, Z. Abou El; Moudoud, H.; Brik, B.
When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network Article de journal
Dans: IEEE Transactions on Consumer Electronics, vol. 71, no 2, p. 7192–7200, 2025, ISSN: 00983063 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Collaborative modeling, Communication efficiency, Differential privacy, Distributed environments, Edge consumer network, EDGE Networks, Federated learning, Knowledge distillation, Model training, Secure multi-party computation, Security challenges, TinyML
@article{abouelhoudaWhenFederatedLearning2025,
title = {When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network},
author = {Z. Abou El Houda and H. Moudoud and B. Brik},
url = {https://www.scopus.com/pages/publications/105002607319?origin=resultslist},
doi = {10.1109/TCE.2025.3559004},
issn = {00983063 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {71},
number = {2},
pages = {7192–7200},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Consumer networks face several security challenges due to the distributed nature of edge devices and the sensitive data they handle. Federated Learning (FL) presents a promising paradigm for collaborative model training in distributed environments. However, its implementation in edge consumer networks raises concerns about model heterogeneity, communication efficiency, and reverse engineering attacks. To address these issues, in this paper, we introduce SKDFL, a novel framework that leverages Knowledge Distillation (KD) and Secure Multi-Party Computation (SMPC) techniques to enhance communication efficiency while preserving data privacy in edge consumer networks. Through the use of KD, the distilled knowledge is transmitted between devices, significantly reducing communication overhead. Additionally, we incorporate lightweight encryption mechanisms to protect soft-labels from reverse engineering attacks using SMPC. We evaluate our proposed framework using two public datasets and demonstrate its efficiency in reducing communication costs, achieving up to a 92.4% reduction compared to conventional FL methods. Moreover, SKDFL achieves high performances in terms of accuracy and F1-score in both binary and multi-class classification while preserving the privacy of clients. Our obtained results show the potential of SKDFL to address the challenges of communication efficiency and data privacy in FL for edge consumer networks, paving the way for secure and efficient collaborative learning in consumer networks. © 1975-2011 IEEE.},
keywords = {Adversarial machine learning, Collaborative modeling, Communication efficiency, Differential privacy, Distributed environments, Edge consumer network, EDGE Networks, Federated learning, Knowledge distillation, Model training, Secure multi-party computation, Security challenges, TinyML},
pubstate = {published},
tppubtype = {article}
}
Consumer networks face several security challenges due to the distributed nature of edge devices and the sensitive data they handle. Federated Learning (FL) presents a promising paradigm for collaborative model training in distributed environments. However, its implementation in edge consumer networks raises concerns about model heterogeneity, communication efficiency, and reverse engineering attacks. To address these issues, in this paper, we introduce SKDFL, a novel framework that leverages Knowledge Distillation (KD) and Secure Multi-Party Computation (SMPC) techniques to enhance communication efficiency while preserving data privacy in edge consumer networks. Through the use of KD, the distilled knowledge is transmitted between devices, significantly reducing communication overhead. Additionally, we incorporate lightweight encryption mechanisms to protect soft-labels from reverse engineering attacks using SMPC. We evaluate our proposed framework using two public datasets and demonstrate its efficiency in reducing communication costs, achieving up to a 92.4% reduction compared to conventional FL methods. Moreover, SKDFL achieves high performances in terms of accuracy and F1-score in both binary and multi-class classification while preserving the privacy of clients. Our obtained results show the potential of SKDFL to address the challenges of communication efficiency and data privacy in FL for edge consumer networks, paving the way for secure and efficient collaborative learning in consumer networks. © 1975-2011 IEEE.
4.
Saini, H. K.; Rani, S.; Ouaissa, M.; Ouaissa, M.; Houda, Z. A. El; Moudoud, H.
CRC Press, 2025, ISBN: 978-100364032-5 (ISBN); 978-104107046-7 (ISBN), (Journal Abbreviation: Digit. Forensics in Next-Gener. Internet of Med. Things: Balanc. Secur. and Sustain. Pages: 283 Publication Title: Digit. Forensics in Next-Gener. Internet of Med. Things: Balanc. Secur. and Sustain.).
Résumé | Liens | BibTeX | Étiquettes: Case-studies, Computer forensics, Forensic Techniques, Internet of things, Machine data, Medical computing, Medical data, Medical practitioner, Network security, Next generation Internet, Patient data, Real-world, Security challenges, Security solutions, sustainable development
@book{sainiDigitalForensicsNextGeneration2025,
title = {Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability},
author = {H. K. Saini and S. Rani and M. Ouaissa and M. Ouaissa and Z. A. El Houda and H. Moudoud},
url = {https://www.scopus.com/pages/publications/105024400792?origin=resultslist},
doi = {10.1201/9781003640325},
isbn = {978-100364032-5 (ISBN); 978-104107046-7 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {CRC Press},
series = {Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability},
abstract = {This book provides a comprehensive exploration of the security challenges and solutions with digital sustainability in the rapidly evolving digital landscape of digital forensics. It explores the details of protecting Internet of Medical Things (IoMT) environments, where the medical data, patient data, and machine data are at high risk with the digital experiences. The book seeks to provide researchers, medical practitioners, and IT specialists with important information. It aims to set the stage for a future in which security and efficiency in IoMT smoothly blend through real-world case studies. Key themes cover IoMT-specific forensic techniques, the difficulties of striking a balance between environmental responsibility and security, and creative solutions that combine the two viewpoints. © 2026 selection and editorial matter, Hemant Kumar Saini, Sita Rani, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Abou El Houda, and Hajar Moudoud. All rights reserved.},
note = {Journal Abbreviation: Digit. Forensics in Next-Gener. Internet of Med. Things: Balanc. Secur. and Sustain.
Pages: 283
Publication Title: Digit. Forensics in Next-Gener. Internet of Med. Things: Balanc. Secur. and Sustain.},
keywords = {Case-studies, Computer forensics, Forensic Techniques, Internet of things, Machine data, Medical computing, Medical data, Medical practitioner, Network security, Next generation Internet, Patient data, Real-world, Security challenges, Security solutions, sustainable development},
pubstate = {published},
tppubtype = {book}
}
This book provides a comprehensive exploration of the security challenges and solutions with digital sustainability in the rapidly evolving digital landscape of digital forensics. It explores the details of protecting Internet of Medical Things (IoMT) environments, where the medical data, patient data, and machine data are at high risk with the digital experiences. The book seeks to provide researchers, medical practitioners, and IT specialists with important information. It aims to set the stage for a future in which security and efficiency in IoMT smoothly blend through real-world case studies. Key themes cover IoMT-specific forensic techniques, the difficulties of striking a balance between environmental responsibility and security, and creative solutions that combine the two viewpoints. © 2026 selection and editorial matter, Hemant Kumar Saini, Sita Rani, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Abou El Houda, and Hajar Moudoud. All rights reserved.