Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Abdollahi, M.; Zhu, Y.; Guimãraes, H. R.; Coallier, N.; Maucourt, S.; Giovenazzo, P.; Falk, T. H.
On the Prediction of Varroa Mite Infestations in Honey Bee Colonies via Acoustic Monitoring Journal Article
In: IEEE Sensors Journal, vol. 26, no. 8, pp. 12157–12167, 2026, ISSN: 1530437X (ISSN).
Abstract | Links | BibTeX | Tags: Acoustic measuring instruments, Acoustic monitoring, Acoustic variables measurement, Acoustics, Beehive acoustic, Beehive acoustics, Ecosystems, Food products, Honey bee, honey bees, Honeybee colonies, Learning systems, Machine learning approaches, Modulation spectrogram, Motion compensation, Nearest neighbor search, Parasite-, Random forests, Sanitary sewers, Spectrograms, Spectrographs, Support vector machines, Varroa destructor, Varroa mite infestation
@article{abdollahiPredictionVarroaMite2026,
title = {On the Prediction of Varroa Mite Infestations in Honey Bee Colonies via Acoustic Monitoring},
author = {M. Abdollahi and Y. Zhu and H. R. Guimãraes and N. Coallier and S. Maucourt and P. Giovenazzo and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105031477347?origin=resultslist},
doi = {10.1109/JSEN.2026.3666127},
issn = {1530437X (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Sensors Journal},
volume = {26},
number = {8},
pages = {12157–12167},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Honey bees play a vital role in global ecosystems and agriculture through their pollination services. However, the Varroa destructor mite remains one of the most damaging parasites affecting honey bee health, contributing significantly to colony decline. Early detection of Varroa infestation is essential for effective beekeeping management. In this study, we propose a machine learning approach for predicting Varroa mite infestation levels based on the acoustic analysis of hive recordings. We introduce a set of nine spectral shape descriptors (SSDs) computed from conventional and modulation spectrograms to characterize the acoustic environment within hives. We evaluate the predictive power of these features against traditional cepstral features across multiple classifiers, including support vector machines (SVMs), random forests (RFs), and k-nearest neighbors (KNNs). Our results demonstrate that the proposed SSDs significantly improve the classification performance relative to conventional features across numerous figures of merit, particularly in hive-independent test settings. This work highlights the potential of spectral acoustic monitoring combined with the supervised learning as a scalable and noninvasive tool for precision beekeeping and early detection of parasitic threats. © 2001-2012 IEEE.},
keywords = {Acoustic measuring instruments, Acoustic monitoring, Acoustic variables measurement, Acoustics, Beehive acoustic, Beehive acoustics, Ecosystems, Food products, Honey bee, honey bees, Honeybee colonies, Learning systems, Machine learning approaches, Modulation spectrogram, Motion compensation, Nearest neighbor search, Parasite-, Random forests, Sanitary sewers, Spectrograms, Spectrographs, Support vector machines, Varroa destructor, Varroa mite infestation},
pubstate = {published},
tppubtype = {article}
}
Honey bees play a vital role in global ecosystems and agriculture through their pollination services. However, the Varroa destructor mite remains one of the most damaging parasites affecting honey bee health, contributing significantly to colony decline. Early detection of Varroa infestation is essential for effective beekeeping management. In this study, we propose a machine learning approach for predicting Varroa mite infestation levels based on the acoustic analysis of hive recordings. We introduce a set of nine spectral shape descriptors (SSDs) computed from conventional and modulation spectrograms to characterize the acoustic environment within hives. We evaluate the predictive power of these features against traditional cepstral features across multiple classifiers, including support vector machines (SVMs), random forests (RFs), and k-nearest neighbors (KNNs). Our results demonstrate that the proposed SSDs significantly improve the classification performance relative to conventional features across numerous figures of merit, particularly in hive-independent test settings. This work highlights the potential of spectral acoustic monitoring combined with the supervised learning as a scalable and noninvasive tool for precision beekeeping and early detection of parasitic threats. © 2001-2012 IEEE.
2.
Soultana, O. A.; Moudoud, H.
Adaptive Heterogeneous Ensemble Learning for Attack Detection in IoT Networks Proceedings Article
In: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., pp. 27–32, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.