@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}

}

@inproceedings{boudraLightweightIoCExtraction2025,

title = {Toward Lightweight IoC Extraction in IoT: The Role of Small Language Models},

author = {N. Boudra and A. Elhajjout and H. Moudoud and M. Oujaoura and Z. Jarir and Z. A. El Houda},

url = {https://www.scopus.com/pages/publications/105037622873?origin=resultslist},

doi = {10.1109/SmartAgriSuSY68475.2025.11466843},

isbn = {979-833157801-5 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY},

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

abstract = {The proliferation of cyber threats necessitates rapid and accurate extraction of Indicators of Compromise (IoCs) from diverse security data sources. While Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language processing and information extraction tasks, their deployment on resource-constrained IoT devices remains challenging due to computational and memory requirements. This paper investigates whether Small Language Models (SLMs) can serve as effective alternatives to LLMs for IoC extraction in IoT environments with limited resources. We present a novel direct LLM-based IoC extraction system leveraging context memory mechanisms for document-wide semantic understanding, specifically designed for deployment on edge computing infrastructure with consumer-grade hardware. Our experimental setup utilizes an RTX 4080 GPU and Ryzen 7 7700X processor running the Ollama framework with GPT-OSS:20B model to evaluate the feasibility of using smaller models instead of resource-intensive LLMs for security tasks. Evaluated on 9 diverse threat intelligence reports spanning different malware families and attack campaigns, the system achieved an average F1 score of 0.62, precision of 0.54, recall of 0.79, and accuracy of 0.85, demonstrating that SLMs can achieve acceptable accuracy levels for IoC extraction while operating within the computational constraints typical of IoT edge deployments. The results suggest that smaller models may provide viable alternatives to large language models for distributed threat intelligence processing in resource-limited environments. © 2025 IEEE.},

note = {Journal Abbreviation: Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY},

keywords = {Artificial intelligence, Cyber threats, Data-source, Edge Computing, extraction, Indicator of compromize, Indicators of Compromise, Information retrieval, Information Security, IoT Security, Language model, Malware, Natural languages, Network security, Program processors, Resource Constraint, Resource Constraints, Semantics, Small language model, Small Language Models, Threat Intelligence},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}