@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}

}

@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}

}