Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Mehrban, A.; Moudoud, H.; Brik, B.; Houda, Z. A. El
Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework Article de journal
Dans: IEEE Internet of Things Magazine, vol. 9, no 4, p. 30–36, 2026, ISSN: 25763180 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Air grounds, Bandwidth, Computation offloading, Federated learning, federated learning (FL), IEEE Standards, Integrated networks, Intelligent reflecting surface, Intelligent reflecting surfaces (IRS), Intelligent transportation system, Intelligent transportation systems, intelligent transportation systems (ITS), Intelligent vehicle highway systems, Learning systems, Reflecting surface, Semantic offloading, Semantics, Sensor networks, Sensors data, Signal encoding, Space-air-ground integrated network, space-air-ground integrated networks (SAGIN), Traffic congestion, Transmissions
@article{mehrbanEnhancingSpectralEfficiency2026,
title = {Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework},
author = {A. Mehrban and H. Moudoud and B. Brik and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105036681552?origin=resultslist},
doi = {10.1109/MIOT.2026.3676349},
issn = {25763180 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Internet of Things Magazine},
volume = {9},
number = {4},
pages = {30–36},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In dense Vehicle-to-Everything (V2X) scenarios within Space-Air-Ground Integrated Networks (SAGIN), connected autonomous cars (CAVs) generate a large volume of sensor data, which leads to significant bandwidth congestion. There is a risk to safety because of the poor Packet Success Rate (PSR) and dangerous transmission delays caused by this congestion. To address this issue, we introduce SemantIRS, a framework that integrates semantic offloading with Intelligent Reflecting Surface (IRS)-assisted links to achieve high network resilience, when compute resources permit and lower latency. SemantIRS deploys lightweight Large Language Models (LLMs) as encoders to compress massive sensor data at the edge of the SAGIN network before transmission through IRS-assisted links. Intent-Based Networking (IBN) and Federated Learning (FL) are also integrated to automate resilience policies and preserve distributed offloading privacy throughout SAGIN layers, respectively. We conduct our experiments using a 12-state hardware benchmark of three LLM encoders (Phi-2, Llama-2-7B-Chat, and Mistral-7B-Instruct-v0.2) on four compute tiers (TPU v6e-1, A100, L4, and Intel Xeon CPU). The measured inference latencies are then fed into an IEEE 802.11p airtime simulator. Our obtained results show that, in congested network conditions, SemantIRS improves the packet success rate from 1.7% to 66.4% while reducing transmission energy by 90%. © 2026 IEEE.},
keywords = {Air grounds, Bandwidth, Computation offloading, Federated learning, federated learning (FL), IEEE Standards, Integrated networks, Intelligent reflecting surface, Intelligent reflecting surfaces (IRS), Intelligent transportation system, Intelligent transportation systems, intelligent transportation systems (ITS), Intelligent vehicle highway systems, Learning systems, Reflecting surface, Semantic offloading, Semantics, Sensor networks, Sensors data, Signal encoding, Space-air-ground integrated network, space-air-ground integrated networks (SAGIN), Traffic congestion, Transmissions},
pubstate = {published},
tppubtype = {article}
}
In dense Vehicle-to-Everything (V2X) scenarios within Space-Air-Ground Integrated Networks (SAGIN), connected autonomous cars (CAVs) generate a large volume of sensor data, which leads to significant bandwidth congestion. There is a risk to safety because of the poor Packet Success Rate (PSR) and dangerous transmission delays caused by this congestion. To address this issue, we introduce SemantIRS, a framework that integrates semantic offloading with Intelligent Reflecting Surface (IRS)-assisted links to achieve high network resilience, when compute resources permit and lower latency. SemantIRS deploys lightweight Large Language Models (LLMs) as encoders to compress massive sensor data at the edge of the SAGIN network before transmission through IRS-assisted links. Intent-Based Networking (IBN) and Federated Learning (FL) are also integrated to automate resilience policies and preserve distributed offloading privacy throughout SAGIN layers, respectively. We conduct our experiments using a 12-state hardware benchmark of three LLM encoders (Phi-2, Llama-2-7B-Chat, and Mistral-7B-Instruct-v0.2) on four compute tiers (TPU v6e-1, A100, L4, and Intel Xeon CPU). The measured inference latencies are then fed into an IEEE 802.11p airtime simulator. Our obtained results show that, in congested network conditions, SemantIRS improves the packet success rate from 1.7% to 66.4% while reducing transmission energy by 90%. © 2026 IEEE.
2.
Amari, H.; Houda, Z. A. El; Moudoud, H.; Khoukhi, L.; Belguith, L. H.
Blockchain-Based Federated Learning for Enhanced Cyber-Threats Detection in Connected Vehicles Article d'actes
Dans: M., Valenti; D., Reed; M., Torres (Ed.): IEEE Int Conf Commun, p. 4257–4262, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 15503607 (ISSN); 979-833150521-9 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Résumé | Liens | BibTeX | Étiquettes: Block-chain, Blockchain, Central layers, Computer crime, Connected vehicle, Connected Vehicles, Cyber threats, Cyberthreat detection, Cyberthreats, Cyberthreats Detection, Data privacy, Federated learning, Intelligent transport, Intelligent vehicle highway systems, Internet of things, Intrusion Detection, Intrusion Detection Systems, Learning systems, Network security, SDN, Threat detection, Traffic control, Vehicles
@inproceedings{amariBlockchainBasedFederatedLearning2025,
title = {Blockchain-Based Federated Learning for Enhanced Cyber-Threats Detection in Connected Vehicles},
author = {H. Amari and Z. A. El Houda and H. Moudoud and L. Khoukhi and L. H. Belguith},
editor = {Valenti M. and Reed D. and Torres M.},
url = {https://www.scopus.com/pages/publications/105018456633?origin=resultslist},
doi = {10.1109/ICC52391.2025.11161266},
isbn = {15503607 (ISSN); 979-833150521-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IEEE Int Conf Commun},
pages = {4257–4262},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Over the past few years, there have been made significant strides in advancing the Internet of Vehicles (IoV), recognizing its strategic importance in Intelligent Transport Systems. The proliferation of connected and autonomous vehicles on the roads has propelled the IoV into the spotlight. However, addressing the specific demands of vehicular networks, such as low latency, high mobility, extensive connectivity of 5G/6G networks, and robust security, remains a substantial challenge. Therefore, there is a critical need for substantial progress in implementing a resilient Intrusion Detection System within the IoV ecosystem. This paper introduces VFed-IDS, a decentralized, secure, flexible, scalable, and robust Blockchain and Federated Learning-based intrusion detection system. VFed-IDS is designed to identify cyber threats in the IoV while preserving privacy in connected vehicles. The proposed architecture consists of three main layers: the central layer, the local layer, and the Blockchain layer. The central layer includes the SDN Controller, responsible for training and aggregating the global model. The local layer comprises vehicles training individual models based on their private local datasets. The Blockchain layer introduces the Smart Contract VFed-SC, which manages the list of authenticated and collaborating vehicles in the Federated Learning process. It also hashes trained local model updates before transmitting them as transactions between the central and local layers. Simulation results demonstrate that VFed-IDS achieves a high accuracy rate of 99%, effectively enhancing the autonomous behavior of connected vehicles against cyber threats. © 2025 IEEE.},
note = {Journal Abbreviation: IEEE Int Conf Commun},
keywords = {Block-chain, Blockchain, Central layers, Computer crime, Connected vehicle, Connected Vehicles, Cyber threats, Cyberthreat detection, Cyberthreats, Cyberthreats Detection, Data privacy, Federated learning, Intelligent transport, Intelligent vehicle highway systems, Internet of things, Intrusion Detection, Intrusion Detection Systems, Learning systems, Network security, SDN, Threat detection, Traffic control, Vehicles},
pubstate = {published},
tppubtype = {inproceedings}
}
Over the past few years, there have been made significant strides in advancing the Internet of Vehicles (IoV), recognizing its strategic importance in Intelligent Transport Systems. The proliferation of connected and autonomous vehicles on the roads has propelled the IoV into the spotlight. However, addressing the specific demands of vehicular networks, such as low latency, high mobility, extensive connectivity of 5G/6G networks, and robust security, remains a substantial challenge. Therefore, there is a critical need for substantial progress in implementing a resilient Intrusion Detection System within the IoV ecosystem. This paper introduces VFed-IDS, a decentralized, secure, flexible, scalable, and robust Blockchain and Federated Learning-based intrusion detection system. VFed-IDS is designed to identify cyber threats in the IoV while preserving privacy in connected vehicles. The proposed architecture consists of three main layers: the central layer, the local layer, and the Blockchain layer. The central layer includes the SDN Controller, responsible for training and aggregating the global model. The local layer comprises vehicles training individual models based on their private local datasets. The Blockchain layer introduces the Smart Contract VFed-SC, which manages the list of authenticated and collaborating vehicles in the Federated Learning process. It also hashes trained local model updates before transmitting them as transactions between the central and local layers. Simulation results demonstrate that VFed-IDS achieves a high accuracy rate of 99%, effectively enhancing the autonomous behavior of connected vehicles against cyber threats. © 2025 IEEE.