Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Journal Article
In: IEEE Open Journal of the Computer Society, vol. 7, pp. 492–503, 2026, ISSN: 26441268 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadeh_vision-based_2026,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029942397&doi=10.1109%2FOJCS.2026.3663268&partnerID=40&md5=b11484e035458c84b1d3f6780b92c91c},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
2.
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.
3.
Tiwari, A.; Arrabito, R.; Davoust, A.; Falk, T. H.
Bias in Physiology-Based Cognitive State Detection for Human–Autonomy Teaming: A Comprehensive Survey Proceedings Article
In: R.A., Sottilare; J., Schwarz (Ed.): Lect. Notes Comput. Sci., pp. 139–158, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303230014-0 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Behavioral research, Bias, Biases, Biomedical signal processing, Cognitive state, Cognitive systems, Human Autonomy Teaming, Instructional system, Intelligent systems, Learning pathway, Learning systems, Personalized learning, Physiological models, Physiological signals, Population statistics, Psychophysiology, State Detection, Student feedback, Surveying, Wearable devices, Wearable technology
@inproceedings{tiwariBiasPhysiologyBasedCognitive2026,
title = {Bias in Physiology-Based Cognitive State Detection for Human–Autonomy Teaming: A Comprehensive Survey},
author = {A. Tiwari and R. Arrabito and A. Davoust and T. H. Falk},
editor = {Sottilare R.A. and Schwarz J.},
url = {https://www.scopus.com/pages/publications/105044000677?origin=resultslist},
doi = {10.1007/978-3-032-30015-7_9},
isbn = {03029743 (ISSN); 978-303230014-0 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16737 LNCS},
pages = {139–158},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Advances in artificial intelligence (AI) are changing the role of adaptive instructional systems (AIS) from passive tools, that deliver personalized learning pathways by adapting to student feedback, to active team members and co-learners that dynamically adapt alongside human partners. In this emerging landscape of human–autonomy teaming (HAT), assessing the cognitive states of individuals interacting with highly intelligent systems has become a critical design consideration for improving team performance, collaboration, and learning outcomes. Physiological signals have emerged as a popular method for measurement of cognitive states in the past few decades. However, these signals can be strongly influenced by different user demographics, including age and biological sex. If unaccounted for, these differences could introduce systematic biases into AIS based learning pathways for different demographic groups. These biases can lead to discriminative performance of instructional systems and may also leave them vulnerable to adversarial exploitation. Aside from physiological signal-induced biases, there may also be major behavioural differences between different sex and/or age groups when interacting with and teaming with AIS, thus further confounding cognitive state monitoring. In this survey, we review emerging human-autonomy teaming studies that incorporate physiological data and analyze where biological sex- and age-related physiological or behavioral differences were documented and/or analyzed. We highlight evidence demonstrating that differences do exist during interaction with autonomous systems. We then discuss how biases can accumulate across study design and analysis modelling pipelines, and provide practical guidelines for mitigating these biases in future human-autonomy teaming research and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Behavioral research, Bias, Biases, Biomedical signal processing, Cognitive state, Cognitive systems, Human Autonomy Teaming, Instructional system, Intelligent systems, Learning pathway, Learning systems, Personalized learning, Physiological models, Physiological signals, Population statistics, Psychophysiology, State Detection, Student feedback, Surveying, Wearable devices, Wearable technology},
pubstate = {published},
tppubtype = {inproceedings}
}
Advances in artificial intelligence (AI) are changing the role of adaptive instructional systems (AIS) from passive tools, that deliver personalized learning pathways by adapting to student feedback, to active team members and co-learners that dynamically adapt alongside human partners. In this emerging landscape of human–autonomy teaming (HAT), assessing the cognitive states of individuals interacting with highly intelligent systems has become a critical design consideration for improving team performance, collaboration, and learning outcomes. Physiological signals have emerged as a popular method for measurement of cognitive states in the past few decades. However, these signals can be strongly influenced by different user demographics, including age and biological sex. If unaccounted for, these differences could introduce systematic biases into AIS based learning pathways for different demographic groups. These biases can lead to discriminative performance of instructional systems and may also leave them vulnerable to adversarial exploitation. Aside from physiological signal-induced biases, there may also be major behavioural differences between different sex and/or age groups when interacting with and teaming with AIS, thus further confounding cognitive state monitoring. In this survey, we review emerging human-autonomy teaming studies that incorporate physiological data and analyze where biological sex- and age-related physiological or behavioral differences were documented and/or analyzed. We highlight evidence demonstrating that differences do exist during interaction with autonomous systems. We then discuss how biases can accumulate across study design and analysis modelling pipelines, and provide practical guidelines for mitigating these biases in future human-autonomy teaming research and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
4.
Elhajjout, A.; Houda, Z. A. E.; Moudoud, H.; Brik, B.; Jan, M. A.
Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions Journal Article
In: IEEE Journal of Biomedical and Health Informatics, 2026, ISSN: 21682194 (ISSN).
Abstract | Links | BibTeX | Tags: Application research, Artificial intelligence, digital health, Distributed computer systems, Federated learning, Fine tuning, Health care, Health care application, Healthcare AI, human, Internet, Internet of medical thing, Internet of Medical Things, Language model, Large language model, large language models, Learning systems, male, Medical computing, natural language processing, Parameter-efficient fine-tuning, patient coding, Privacy, Privacy preservation, Privacy preserving, Privacy-preserving techniques, review, Sensitive data, trustworthiness
@article{elhajjoutFederatedLargeLanguage2026,
title = {Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions},
author = {A. Elhajjout and Z. A. E. Houda and H. Moudoud and B. Brik and M. A. Jan},
url = {https://www.scopus.com/pages/publications/105034640126?origin=resultslist},
doi = {10.1109/JBHI.2026.3679612},
issn = {21682194 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Large-scale foundation models, especially Federated Large Language Models (FLLMs), aim to transform digital health by enabling clinically-grade natural language processing while keeping sensitive data local. However, their adoption is hindered by two main issues: (i) the computational and communication burden of parameter-rich models on resource-constrained Internet-of-Medical-Things (IoMT) devices, and (ii) performance degradation caused by Non-Independent and Identically Distributed (Non-IID) patient data. This paper presents a comprehensive survey of Federated Learning (FL) for LLMs in Healthcare (FedMed-LLMs). We review the foundations of FL and medical LLMs. Then, we present the FL-enabled LLMs applications in healthcare, and we examine their issues in terms of privacy, robustness, and trustworthiness. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions for building robust and scalable FedMed-LLMs systems. © 2013 IEEE.},
keywords = {Application research, Artificial intelligence, digital health, Distributed computer systems, Federated learning, Fine tuning, Health care, Health care application, Healthcare AI, human, Internet, Internet of medical thing, Internet of Medical Things, Language model, Large language model, large language models, Learning systems, male, Medical computing, natural language processing, Parameter-efficient fine-tuning, patient coding, Privacy, Privacy preservation, Privacy preserving, Privacy-preserving techniques, review, Sensitive data, trustworthiness},
pubstate = {published},
tppubtype = {article}
}
Large-scale foundation models, especially Federated Large Language Models (FLLMs), aim to transform digital health by enabling clinically-grade natural language processing while keeping sensitive data local. However, their adoption is hindered by two main issues: (i) the computational and communication burden of parameter-rich models on resource-constrained Internet-of-Medical-Things (IoMT) devices, and (ii) performance degradation caused by Non-Independent and Identically Distributed (Non-IID) patient data. This paper presents a comprehensive survey of Federated Learning (FL) for LLMs in Healthcare (FedMed-LLMs). We review the foundations of FL and medical LLMs. Then, we present the FL-enabled LLMs applications in healthcare, and we examine their issues in terms of privacy, robustness, and trustworthiness. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions for building robust and scalable FedMed-LLMs systems. © 2013 IEEE.
5.
Mehrban, A.; Moudoud, H.; Brik, B.; Houda, Z. A. El
Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework Journal Article
In: IEEE Internet of Things Magazine, vol. 9, no. 4, pp. 30–36, 2026, ISSN: 25763180 (ISSN).
Abstract | Links | BibTeX | Tags: 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.
6.
Selamnia, A.; Moudoud, H.; Khoukhi, L.; Brik, B.; Houda, Z. A. El
QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks Proceedings Article
In: IEEE Int Conf Commun, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 15503607 (ISSN); 979-831954209-0 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Abstract | Links | BibTeX | Tags: Automation, Complex networks, Cyber threats, Federated learning, IIoT, Industrial automation, Industrial internet of thing, Intrusion Detection, Intrusion-Detection, Learning systems, Machine learning methods, Network intrusion, Network security, Privacy-preserving techniques, Processing power, QML, Quantum circuit, Quantum entanglement, Split Learning, Variational quantum circuit, VQC
@inproceedings{selamniaQSFLIDQuantumSplitFederated2026,
title = {QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks},
author = {A. Selamnia and H. Moudoud and L. Khoukhi and B. Brik and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105045419288?origin=resultslist},
doi = {10.1109/ICC59461.2026.11587037},
isbn = {15503607 (ISSN); 979-831954209-0 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Int Conf Commun},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The Industrial Internet of Things (IIoT) is reshaping industrial automation through interconnected, intelligent systems. However, this evolution increases exposure to sophisticated cyber threats, especially given the constraints of IIoT devices such as limited processing power, bandwidth, and heterogeneous protocols. Traditional machine learning methods often fail to meet these security demands due to their computational intensity and centralized data requirements. To address this, we propose a hybrid quantum-classical Split Federated Learning (SFL) framework for intrusion detection in IIoT networks. Our method integrates Variational Quantum Circuits (VQCs) to model complex, non-linear data relationships, enhancing detection accuracy while preserving data privacy through decentralized learning. The architecture assigns lightweight preprocessing to edge devices and complex analysis to a quantum backend, ensuring efficiency and scalability. To evaluate the proposed framework, we conduct extensive experiments on the real-world EDGE-IIoT dataset; the experimental results demonstrate that the model attains 95.5% training accuracy, significantly surpassing classical SFL (85.7%). In addition, the quantum model's enhanced entanglement properties and expressibility strengthen its generalization performance. This approach offers an efficient and privacy-preserving solution for securing IIoT systems. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Int Conf Commun},
keywords = {Automation, Complex networks, Cyber threats, Federated learning, IIoT, Industrial automation, Industrial internet of thing, Intrusion Detection, Intrusion-Detection, Learning systems, Machine learning methods, Network intrusion, Network security, Privacy-preserving techniques, Processing power, QML, Quantum circuit, Quantum entanglement, Split Learning, Variational quantum circuit, VQC},
pubstate = {published},
tppubtype = {inproceedings}
}
The Industrial Internet of Things (IIoT) is reshaping industrial automation through interconnected, intelligent systems. However, this evolution increases exposure to sophisticated cyber threats, especially given the constraints of IIoT devices such as limited processing power, bandwidth, and heterogeneous protocols. Traditional machine learning methods often fail to meet these security demands due to their computational intensity and centralized data requirements. To address this, we propose a hybrid quantum-classical Split Federated Learning (SFL) framework for intrusion detection in IIoT networks. Our method integrates Variational Quantum Circuits (VQCs) to model complex, non-linear data relationships, enhancing detection accuracy while preserving data privacy through decentralized learning. The architecture assigns lightweight preprocessing to edge devices and complex analysis to a quantum backend, ensuring efficiency and scalability. To evaluate the proposed framework, we conduct extensive experiments on the real-world EDGE-IIoT dataset; the experimental results demonstrate that the model attains 95.5% training accuracy, significantly surpassing classical SFL (85.7%). In addition, the quantum model's enhanced entanglement properties and expressibility strengthen its generalization performance. This approach offers an efficient and privacy-preserving solution for securing IIoT systems. © 2026 IEEE.
7.
Kadi, A.; Moudoud, H.; Khoukhi, L.; Houda, Z. A. El
Quantum-Enhanced LSTM for Sequential Network Flow Analysis: A Hybrid Approach to DDoS Detection Proceedings Article
In: Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC, pp. 830–834, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156110-9 (ISBN), (Journal Abbreviation: Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC).
Abstract | Links | BibTeX | Tags: Denialof- service attacks, Distributed computer systems, Distributed denial of service, Distributed denial-of-service, Distributed Denial-of-Service (DDoS), Hybrid approach, Intrusion Detection, Learning systems, Logic gates, Long short-term memory, Machine-learning, Memory architecture, Network architecture, Network flow analysis, Network security, QLSTM, Quantum entanglement, Quantum machine learning, Quantum Machine Learning(QML), Quantum machines, Qubits, short term memory
@inproceedings{kadiQuantumEnhancedLSTMSequential2026,
title = {Quantum-Enhanced LSTM for Sequential Network Flow Analysis: A Hybrid Approach to DDoS Detection},
author = {A. Kadi and H. Moudoud and L. Khoukhi and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105040813271?origin=resultslist},
doi = {10.1109/QCNC69040.2026.00136},
isbn = {979-833156110-9 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC},
pages = {830–834},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Distributed Denial-of-Service (DDoS) attacks continue to grow at a rapid rate, making timely and reliable detection a challenge for intrusion detection systems (IDSs). Classical deep models such as Long Short-Term Memory (LSTM) can capture temporal dependencies; however, they still struggle with highly nonlinear and noisy flow dynamics, especially when attack patterns shift. To address this, we propose QLSTM-E, an enhanced hybrid quantum-classical architecture for network intrusion detection that uses Variational Quantum Circuits (VQCs) and LSTM modeling to learn complex temporal dependencies in network traffic. QLSTM-E exploits quantum characteristics, including superposition and entanglement, to improve the representation of nonlinear patterns in sequential flow features. To balance expressivity and circuit cost on near-term devices, we adopt an angle-based encoding strategy and a star-topology entangling layout that reduces two-qubit gate overhead while preserving effective quantum correlations. We implement QLSTM-E using PennyLane and Qiskit and evaluate it on the CIC-DDoS2019 dataset under a fully simulated setting, requiring no access to quantum hardware. Experimental results demonstrate strong detection effectiveness, achieving 99.7% accuracy and F1-score, and show strong robustness under depolarizing noise up to p= 0.1. © 2026 IEEE.},
note = {Journal Abbreviation: Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC},
keywords = {Denialof- service attacks, Distributed computer systems, Distributed denial of service, Distributed denial-of-service, Distributed Denial-of-Service (DDoS), Hybrid approach, Intrusion Detection, Learning systems, Logic gates, Long short-term memory, Machine-learning, Memory architecture, Network architecture, Network flow analysis, Network security, QLSTM, Quantum entanglement, Quantum machine learning, Quantum Machine Learning(QML), Quantum machines, Qubits, short term memory},
pubstate = {published},
tppubtype = {inproceedings}
}
Distributed Denial-of-Service (DDoS) attacks continue to grow at a rapid rate, making timely and reliable detection a challenge for intrusion detection systems (IDSs). Classical deep models such as Long Short-Term Memory (LSTM) can capture temporal dependencies; however, they still struggle with highly nonlinear and noisy flow dynamics, especially when attack patterns shift. To address this, we propose QLSTM-E, an enhanced hybrid quantum-classical architecture for network intrusion detection that uses Variational Quantum Circuits (VQCs) and LSTM modeling to learn complex temporal dependencies in network traffic. QLSTM-E exploits quantum characteristics, including superposition and entanglement, to improve the representation of nonlinear patterns in sequential flow features. To balance expressivity and circuit cost on near-term devices, we adopt an angle-based encoding strategy and a star-topology entangling layout that reduces two-qubit gate overhead while preserving effective quantum correlations. We implement QLSTM-E using PennyLane and Qiskit and evaluate it on the CIC-DDoS2019 dataset under a fully simulated setting, requiring no access to quantum hardware. Experimental results demonstrate strong detection effectiveness, achieving 99.7% accuracy and F1-score, and show strong robustness under depolarizing noise up to p= 0.1. © 2026 IEEE.
8.
Moradi, A.; Falk, T. H.
Benchmarking Foundation Models for Cross-Domain Speaker Profiling Proceedings Article
In: IEEE Conf. Artif. Intell., CAI, pp. 78–84, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156039-3 (ISBN), (Journal Abbreviation: IEEE Conf. Artif. Intell., CAI).
Abstract | Links | BibTeX | Tags: Benchmarking, Continuous speech recognition, Cross-domain, Forecasting, Formant frequency, Foundation models, Learning systems, Linguistics, Multi-attributes, Multi-task learning, Paralinguistic, Performance, Speaker identification, Speaker verification, Speech communication, State of the art, Verification task
@inproceedings{moradiBenchmarkingFoundationModels2026,
title = {Benchmarking Foundation Models for Cross-Domain Speaker Profiling},
author = {A. Moradi and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105042046048?origin=resultslist},
doi = {10.1109/CAI68641.2026.11536565},
isbn = {979-833156039-3 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Conf. Artif. Intell., CAI},
pages = {78–84},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Speech conveys both linguistic and paralinguistic content. While pre-trained speech foundation models have been widely explored for linguistic tasks, such as speech recognition, and for speaker identification and verification tasks, very limited work has been done to test their usefulness for multi-attribute speaker profiling, i.e., simultaneous prediction of biological sex, age, and height from speech. In this paper, we aim to benchmark the performance of four state-of-the-art self-supervised foundation models, namely, WavLM, Wav2vec2, HuBERT, and XLSR-53, under both within- and cross-domain conditions. Each model is employed as a frozen pre-trained feature extractor, with lightweight task-specific heads trained jointly in a multi-task learning framework, and their feature extraction latency is empirically analyzed to assess practical deployment considerations. Experiments on two datasets show that WavLM achieves consistently strong within- and cross-domain performance for biological sex prediction, while Wav2vec2 and XLSR-53 exhibit more consistent performance for the age and height regression tasks in cross-domain settings. Interpretability analysis based on correlations with key acoustic features show (1) WavLM internal representations correlating highly with pitch and formant frequencies, corroborating the improved performance on biological sex prediction, and (2) XLSR-53 correlating highly with the second formant frequency, corroborating the results obtained for age and height. Overall, our analysis shows that pre-trained speech foundation models could serve as useful tools for cross-domain speaker profiling tasks. While no model stood out as a clear winner across all tested physical traits, future work could explore the use of ensemble methods for improved generalizability. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},
keywords = {Benchmarking, Continuous speech recognition, Cross-domain, Forecasting, Formant frequency, Foundation models, Learning systems, Linguistics, Multi-attributes, Multi-task learning, Paralinguistic, Performance, Speaker identification, Speaker verification, Speech communication, State of the art, Verification task},
pubstate = {published},
tppubtype = {inproceedings}
}
Speech conveys both linguistic and paralinguistic content. While pre-trained speech foundation models have been widely explored for linguistic tasks, such as speech recognition, and for speaker identification and verification tasks, very limited work has been done to test their usefulness for multi-attribute speaker profiling, i.e., simultaneous prediction of biological sex, age, and height from speech. In this paper, we aim to benchmark the performance of four state-of-the-art self-supervised foundation models, namely, WavLM, Wav2vec2, HuBERT, and XLSR-53, under both within- and cross-domain conditions. Each model is employed as a frozen pre-trained feature extractor, with lightweight task-specific heads trained jointly in a multi-task learning framework, and their feature extraction latency is empirically analyzed to assess practical deployment considerations. Experiments on two datasets show that WavLM achieves consistently strong within- and cross-domain performance for biological sex prediction, while Wav2vec2 and XLSR-53 exhibit more consistent performance for the age and height regression tasks in cross-domain settings. Interpretability analysis based on correlations with key acoustic features show (1) WavLM internal representations correlating highly with pitch and formant frequencies, corroborating the improved performance on biological sex prediction, and (2) XLSR-53 correlating highly with the second formant frequency, corroborating the results obtained for age and height. Overall, our analysis shows that pre-trained speech foundation models could serve as useful tools for cross-domain speaker profiling tasks. While no model stood out as a clear winner across all tested physical traits, future work could explore the use of ensemble methods for improved generalizability. © 2026 IEEE.
9.
Nouboukpo, A.; Allili, M. S.
Semi-supervised flow-augmented Gaussian mixture VAEs with weighted contrastive learning for out-of-distribution detection Journal Article
In: Knowledge-Based Systems, vol. 347, 2026, ISSN: 09507051 (ISSN).
Abstract | Links | BibTeX | Tags: Auto encoders, Benchmarking, Classification (of information), Clusterings, Contrastive Learning, Flow based, Flow-based GMM clustering, Gaussian distribution, Gaussian-mixtures, Learning algorithms, Learning systems, Out-of-distribution detection, Semantics, Semi-supervised, Semi-supervised learning, uncertainty, Variational autoencoder, Variational autoencoders (VAEs)
@article{nouboukpoSemisupervisedFlowaugmentedGaussian2026,
title = {Semi-supervised flow-augmented Gaussian mixture VAEs with weighted contrastive learning for out-of-distribution detection},
author = {A. Nouboukpo and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105040505017?origin=resultslist},
doi = {10.1016/j.knosys.2026.116312},
issn = {09507051 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Knowledge-Based Systems},
volume = {347},
publisher = {Elsevier B.V.},
abstract = {Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning systems, particularly in high-stakes domains where identifying inputs that deviate from the in-distribution (ID) is critical. However, in real-world scenarios, true OOD data are typically unavailable at training time, and only auxiliary datasets acting as imperfect proxies can be used. This makes fully supervised approaches impractical and potentially biased toward specific anomaly types. To address this limitation, we propose FLoW-ssGMVAE, a semi-supervised generative–discriminative framework that leverages limited proxy anomaly supervision together with abundant unlabeled data for robust OOD detection. Our model integrates a flow-augmented Gaussian Mixture VAE, enabling flexible latent modeling that captures nonlinear and multimodal intra-class distributions. Unlike standard VAEs with unimodal Gaussian priors, it uses class-conditional normalizing flows to better represent complex ID and OOD variability. To further refine the latent space and strengthen ID/OOD separation, our model integrates a weighted contrastive learning objective guided by sample-wise attention scores derived from likelihood uncertainty. This mechanism emphasizes ambiguous or hard-to-classify instances, reinforcing semantic boundaries and reducing false detections. By combining expressive generative modeling with uncertainty-aware discriminative training, our method constructs a structured and interpretable latent space that reliably identifies OOD samples under limited supervision. Experiments on standard benchmarks validate the effectiveness and scalability of our approach, demonstrating state-of-the-art performance with reasonable computational cost. Our implementation will be available at: FLoW-ssGMVAE. © 2026},
keywords = {Auto encoders, Benchmarking, Classification (of information), Clusterings, Contrastive Learning, Flow based, Flow-based GMM clustering, Gaussian distribution, Gaussian-mixtures, Learning algorithms, Learning systems, Out-of-distribution detection, Semantics, Semi-supervised, Semi-supervised learning, uncertainty, Variational autoencoder, Variational autoencoders (VAEs)},
pubstate = {published},
tppubtype = {article}
}
Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning systems, particularly in high-stakes domains where identifying inputs that deviate from the in-distribution (ID) is critical. However, in real-world scenarios, true OOD data are typically unavailable at training time, and only auxiliary datasets acting as imperfect proxies can be used. This makes fully supervised approaches impractical and potentially biased toward specific anomaly types. To address this limitation, we propose FLoW-ssGMVAE, a semi-supervised generative–discriminative framework that leverages limited proxy anomaly supervision together with abundant unlabeled data for robust OOD detection. Our model integrates a flow-augmented Gaussian Mixture VAE, enabling flexible latent modeling that captures nonlinear and multimodal intra-class distributions. Unlike standard VAEs with unimodal Gaussian priors, it uses class-conditional normalizing flows to better represent complex ID and OOD variability. To further refine the latent space and strengthen ID/OOD separation, our model integrates a weighted contrastive learning objective guided by sample-wise attention scores derived from likelihood uncertainty. This mechanism emphasizes ambiguous or hard-to-classify instances, reinforcing semantic boundaries and reducing false detections. By combining expressive generative modeling with uncertainty-aware discriminative training, our method constructs a structured and interpretable latent space that reliably identifies OOD samples under limited supervision. Experiments on standard benchmarks validate the effectiveness and scalability of our approach, demonstrating state-of-the-art performance with reasonable computational cost. Our implementation will be available at: FLoW-ssGMVAE. © 2026
10.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks Journal Article
In: IEEE Transactions on Artificial Intelligence, vol. 7, no. 6, pp. 3146–3159, 2026, ISSN: 26914581 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Air navigation, Aircraft landing, Antennas, Automatic uncrewed aerial vehicle (UAV) navigation, Automatic unmanned aerial vehicle navigation, Autonomous navigation, Behavioral research, Computer vision, decision making, Deep learning, deep ordinal regression, Emergency services, Landing zones, Learning systems, Ordinal regression, regression analysis, Safe landing, Safe landing zone, safe landing zones (SLZs), Semantic segmentation, Semantics, Textures, Three dimensional computer graphics, Unmanned aerial vehicles (UAV), Vehicle navigation, Visual servoing
@article{abdollahzadehVisualSafetyMapping2026,
title = {Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/pages/publications/105023324811?origin=resultslist},
doi = {10.1109/TAI.2025.3635093},
issn = {26914581 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Transactions on Artificial Intelligence},
volume = {7},
number = {6},
pages = {3146–3159},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {As uncrewed aerial vehicles (UAVs) see growing use in civilian applications, reliably identifying safe landing zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce ordinal regression safe landing zones netwrok (OR-SLZNet), an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multilevel safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textbackslashsim0.02 s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft landing, Antennas, Automatic uncrewed aerial vehicle (UAV) navigation, Automatic unmanned aerial vehicle navigation, Autonomous navigation, Behavioral research, Computer vision, decision making, Deep learning, deep ordinal regression, Emergency services, Landing zones, Learning systems, Ordinal regression, regression analysis, Safe landing, Safe landing zone, safe landing zones (SLZs), Semantic segmentation, Semantics, Textures, Three dimensional computer graphics, Unmanned aerial vehicles (UAV), Vehicle navigation, Visual servoing},
pubstate = {published},
tppubtype = {article}
}
As uncrewed aerial vehicles (UAVs) see growing use in civilian applications, reliably identifying safe landing zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce ordinal regression safe landing zones netwrok (OR-SLZNet), an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multilevel safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textbackslashsim0.02 s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.