Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Moudoud, H.; Houda, Z. A. El; Khoukhi, L.; Mouftah, H. T.
An SDN-based Adaptive Ensemble Learning Framework for Intrusion Mitigation in Wireless Networks Proceedings Article
In: M., Valenti; D., Reed; M., Torres (Ed.): IEEE Int Conf Commun, pp. 554–559, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 15503607 (ISSN); 979-833150521-9 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Antennas, Artificial intelligence, Computer crime, Ensemble learning, Intrusion Detection, Intrusion Detection Systems, Jamming, Jamming Attacks, Learning algorithms, Learning frameworks, Network intrusion, Network operations, Radio communication, Security systems, Security threats, Sensors network, Unmanned aerial vehicle, Unmanned Aerial Vehicles, Unmanned aerial vehicles (UAV), Wireless networks, Wireless sensor, Wireless Sensor Networks, Zero-day attack
@inproceedings{moudoudSDNbasedAdaptiveEnsemble2025,
title = {An SDN-based Adaptive Ensemble Learning Framework for Intrusion Mitigation in Wireless Networks},
author = {H. Moudoud and Z. A. El Houda and L. Khoukhi and H. T. Mouftah},
editor = {Valenti M. and Reed D. and Torres M.},
url = {https://www.scopus.com/pages/publications/105018460686?origin=resultslist},
doi = {10.1109/ICC52391.2025.11161745},
isbn = {15503607 (ISSN); 979-833150521-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IEEE Int Conf Commun},
pages = {554–559},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Jamming attacks are among the most critical security threats to Wireless Sensor Networks (WSNs), as they can severely disrupt normal network operations, leading to data loss, network downtime, and reduced system performance. Intrusion Detection Systems (IDSs) have therefore become essential to protect WSNs. However, conventional IDSs often struggle to detect zero-day attacks, creating a significant security gap. To address this, Artificial Intelligence (AI)-based IDSs have been introduced, offering improved detection capabilities but frequently encountering high bias or variance issues, which reduce their reliability. Recently, ensemble learning (EL) has emerged as a promising approach to build more adaptable and data-resilient models by combining multiple learning algorithms. In this context, we propose AdaptiveBoost, an SDN-based Adaptive Ensemble Learning Framework, specifically designed for effective jamming attack detection in WSNs. The SDN integration allows AdaptiveBoost to optimize network traffic flow, identify anomalies in real-time, and adaptively fine-tune detection mechanisms based on current network conditions. We conduct several experiments to evaluate AdaptiveBoost using real-world WSN attacks; using the well-known public network security dataset, WSN-DS, show that AdaptiveBoost outperforms AI-based algorithms in terms of accuracy, precision, recall, and F1 score, while achieving a remarkable reduction in training time by a factor of 235, making it an efficient, scalable solution for securing WSNs against jamming attacks. © 2025 IEEE.},
note = {Journal Abbreviation: IEEE Int Conf Commun},
keywords = {Aerial vehicle, Antennas, Artificial intelligence, Computer crime, Ensemble learning, Intrusion Detection, Intrusion Detection Systems, Jamming, Jamming Attacks, Learning algorithms, Learning frameworks, Network intrusion, Network operations, Radio communication, Security systems, Security threats, Sensors network, Unmanned aerial vehicle, Unmanned Aerial Vehicles, Unmanned aerial vehicles (UAV), Wireless networks, Wireless sensor, Wireless Sensor Networks, Zero-day attack},
pubstate = {published},
tppubtype = {inproceedings}
}
Jamming attacks are among the most critical security threats to Wireless Sensor Networks (WSNs), as they can severely disrupt normal network operations, leading to data loss, network downtime, and reduced system performance. Intrusion Detection Systems (IDSs) have therefore become essential to protect WSNs. However, conventional IDSs often struggle to detect zero-day attacks, creating a significant security gap. To address this, Artificial Intelligence (AI)-based IDSs have been introduced, offering improved detection capabilities but frequently encountering high bias or variance issues, which reduce their reliability. Recently, ensemble learning (EL) has emerged as a promising approach to build more adaptable and data-resilient models by combining multiple learning algorithms. In this context, we propose AdaptiveBoost, an SDN-based Adaptive Ensemble Learning Framework, specifically designed for effective jamming attack detection in WSNs. The SDN integration allows AdaptiveBoost to optimize network traffic flow, identify anomalies in real-time, and adaptively fine-tune detection mechanisms based on current network conditions. We conduct several experiments to evaluate AdaptiveBoost using real-world WSN attacks; using the well-known public network security dataset, WSN-DS, show that AdaptiveBoost outperforms AI-based algorithms in terms of accuracy, precision, recall, and F1 score, while achieving a remarkable reduction in training time by a factor of 235, making it an efficient, scalable solution for securing WSNs against jamming attacks. © 2025 IEEE.
2.
Khosrojerdi, F.; Gagnon, S.; Valverde, R.
Applications of Artificial Intelligence in Smart Grids: Present and Future Research Domains Proceedings Article
In: 2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021, pp. 7–12, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-073814535-8 (ISBN), (Journal Abbreviation: IEEE Int. Conf. Smart Energy Grid Eng., SEGE).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
3.
Khosrojerdi, F.; Gagnon, S.; Valverde, R.
Applications of Artificial Intelligence in Smart Grids: Present and Future Research Domains Proceedings Article
In: 2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021, pp. 7–12, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-073814535-8 (ISBN), (Journal Abbreviation: IEEE Int. Conf. Smart Energy Grid Eng., SEGE).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.