@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}

}

@inproceedings{khosrojerdi_applications_2021,

title = {Applications of Artificial Intelligence in Smart Grids: Present and Future Research Domains},

author = {F. Khosrojerdi and S. Gagnon and R. Valverde},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115988707&doi=10.1109%2fSEGE52446.2021.9534914&partnerID=40&md5=a55eba3a0bc91b6381600a3a24de963f},

doi = {10.1109/SEGE52446.2021.9534914},

isbn = {978-073814535-8 (ISBN)},

year  = {2021},

date = {2021-01-01},

booktitle = {2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021},

pages = {7–12},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the last decade, Artificial Intelligence (AI) have been applied overwhelmingly in various research domains in the context of smart grid. It has been one of the main streams of advanced technological approaches that the research community offered for developing smart grids. However, the broad scope of the subject matter has launched complexity for scholars to identify effective research approaches. In this paper, we present a literature review about utilizing AI in the key elements of smart grids including grid-connected vehicles, data-driven components, and the power system network. This will result in highlighting technical challenges of the integration of electric vehicles to the grid and the power network operation as well. Moreover, we discuss the four key research areas in the context of AI and its applications in intelligent power grids. The proposed research fields aid PhD candidates to consider these areas as the promising domains for investigation. © 2021 IEEE.},

note = {Journal Abbreviation: IEEE Int. Conf. Smart Energy Grid Eng., SEGE},

keywords = {AI research, Artificial intelligence, Artificial intelligence research, Electric network analysis, Electric power transmission networks, EV, Network operations, Power network operation, Power networks, Research domains, Research opportunities, Scholar, scholars, Smart grid, Vehicle to grids, Vehicle-to-grid, Vehicles},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{khosrojerdiApplicationsArtificialIntelligence2021,

title = {Applications of Artificial Intelligence in Smart Grids: Present and Future Research Domains},

author = {F. Khosrojerdi and S. Gagnon and R. Valverde},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115988707&doi=10.1109%2fSEGE52446.2021.9534914&partnerID=40&md5=a55eba3a0bc91b6381600a3a24de963f},

doi = {10.1109/SEGE52446.2021.9534914},

isbn = {978-073814535-8 (ISBN)},

year  = {2021},

date = {2021-01-01},

booktitle = {2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021},

pages = {7–12},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the last decade, Artificial Intelligence (AI) have been applied overwhelmingly in various research domains in the context of smart grid. It has been one of the main streams of advanced technological approaches that the research community offered for developing smart grids. However, the broad scope of the subject matter has launched complexity for scholars to identify effective research approaches. In this paper, we present a literature review about utilizing AI in the key elements of smart grids including grid-connected vehicles, data-driven components, and the power system network. This will result in highlighting technical challenges of the integration of electric vehicles to the grid and the power network operation as well. Moreover, we discuss the four key research areas in the context of AI and its applications in intelligent power grids. The proposed research fields aid PhD candidates to consider these areas as the promising domains for investigation. © 2021 IEEE.},

note = {Journal Abbreviation: IEEE Int. Conf. Smart Energy Grid Eng., SEGE},

keywords = {AI research, Artificial intelligence, Artificial intelligence research, Electric network analysis, Electric power transmission networks, EV, Network operations, Power network operation, Power networks, Research domains, Research opportunities, Scholar, scholars, Smart grid, Vehicle to grids, Vehicle-to-grid, Vehicles},

pubstate = {published},

tppubtype = {inproceedings}

}