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# RSEC

Laboratory

## Sitemaps

- [XML Sitemap](https://rseclab.com/sitemap.xml): Contains all public & indexable URLs for this website.

## Posts

- [Blog](https://rseclab.com/blog/)
- [Lightweight LLM Adaptation for Intrusion Detection via Token-Efficient Flow Representation](https://rseclab.com/lightweight-llm-adaptation-for-intrusion-detection-via-token-efficient-flow-representation/) - We are happy to introduce a new study that adapts large language models (LLMs) for intrusion detection in resource-constrained IoT environments. As IoT devices continue to multiply, traditional intrusion detection systems often struggle to keep pace with the diversity and scale of modern network attacks, while full-scale LLMs remain too costly to deploy on limited
- [Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework](https://rseclab.com/enhancing-spectral-efficiency-and-resilience-in-sagin-its-an-irs-assisted-semantic-offloading-framework/) - We are pleased to introduce a new study addressing one of the key challenges in next-generation intelligent transportation systems (ITS): keeping satellite-air-ground integrated networks (SAGIN) both efficient and resilient as they carry growing volumes of connected-vehicle traffic. Conventional offloading strategies often struggle with limited spectral resources and unstable links across satellite, aerial, and terrestrial segments.
- [Quantum-Enhanced LSTM for Sequential Network Flow Analysis: A Hybrid Approach to DDoS Detection](https://rseclab.com/quantum-enhanced-lstm-for-sequential-network-flow-analysis-a-hybrid-approach-to-ddos-detection/) - We are excited to introduce a new study combining quantum computing techniques with deep learning to strengthen network defense against distributed denial-of-service (DDoS) attacks. DDoS traffic often unfolds as complex temporal patterns that are difficult for classical models to capture efficiently at scale, especially in high-throughput network environments. The paper introduces a hybrid quantum-classical LSTM
- [Leveraging Large Language Models for Contextual Threat Hypothesis Generation in IoT Networks](https://rseclab.com/leveraging-large-language-models-for-contextual-threat-hypothesis-generation-in-iot-networks/) - We are happy to introduce a new study exploring how large language models can support threat hunting in IoT environments. Security analysts investigating IoT networks are often overwhelmed by large volumes of raw telemetry and alerts, making it time-consuming to identify which signals point to genuine, evolving threats. The paper proposes a method that leverages
- [Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions](https://rseclab.com/federated-large-language-models-for-a-trustworthy-and-privacy-preserving-healthcare-applications-challenges-and-future-research-directions/) - We are delighted to introduce a comprehensive survey examining how federated large language models (LLMs) can support privacy-preserving, trustworthy healthcare systems. Healthcare data is highly sensitive and often siloed across institutions, making it difficult to train powerful LLMs without compromising patient privacy or violating regulatory constraints. The survey reviews how federated learning allows multiple healthcare
- [RSEC Lab at ICC 2026 in Glasgow, with Four Research Papers!](https://rseclab.com/selfadv-da-unsupervised-domain-adaptation-framework-for-robust-intrusion-detection-in-iot-networks/) - We are pleased to announce that our paper entitled “SelfAdv-DA: Unsupervised Domain Adaptation Framework for Robust Intrusion Detection in IoT Networks” was accepted and presented at IEEE ICC 2026 (IEEE International Conference on Communications), held in Glasgow, Scotland. The paper was authored by Ines Guerziz, Zakaria Abou El Houda, and Long Bao Le, and was presented by Ines Guerziz at the conference.
- [Exciting News: Our Paper on Quantum-Classical Encoding for Intrusion Detection is Accepted in IEEE OJ-COMS!](https://rseclab.com/exciting-news-our-paper-on-quantum-classical-encoding-for-intrusion-detection-is-accepted-in-ieee-oj-coms/) - RSEC Visiting PhD Researchers Adam Kadi and Aymene Selamnia Have Their Paper Accepted in the Prestigious IEEE Open Journal of the Communications Society (OJ-COMS) (Impact Factor: 6.3)
- [Two Best Paper Awards at #IEEE iMETA 2024 & #ICACTCE'24](https://rseclab.com/best-paper-awards-at-icactce24/) - RSEC Lab is honored that our research papers have been recognized with Best Paper Awards at prestigious international conferences!
- [Maroua Cherifi Shines with Award-Winning Work in Federated Learning for Healthcare](https://rseclab.com/maroua-cherifi/) - Congratulations to Maroua Cherifi for her outstanding achievement in earning second place in the computer science competition at the University of Quebec in Trois-Rivières! This well-deserved recognition highlights her exceptional work on the integration of federated learning in healthcare. Her innovative contributions are truly inspiring and pave the way for transformative advancements in the field. Bravo, Maroua! 🎉👏

## Pages

- [Home](https://rseclab.com/) - RSEC Lab Resilient cyberSECurity Research Laboratory Explore more Innovative Research with No Boundaries At RSEC Laboratory, we believe in the power of open collaboration and cutting-edge research. Our projects are driven by innovation and are not confined by traditional boundaries, ensuring that all advancements are accessible and impactful. Developing tomorrow’s intelligent systems today. Protecting digital
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- [Home](https://rseclab.com/home-2/) - RSEC Lab Resilient cyberSECurity Research Laboratory Explore more Innovative Research with No Boundaries At RSEC Laboratory, we believe in the power of open collaboration and cutting-edge research. Our projects are driven by innovation and are not confined by traditional boundaries, ensuring that all advancements are accessible and impactful. Developing tomorrow’s intelligent systems today. Protecting digital
- [Members](https://rseclab.com/team/) - PROFESSOR Prof. Zakaria Abou El Houda zakaria.abouelhouda@inrs.ca Zakaria Abou El Houda is an Assistant Professor at the Energy, Materials, and Telecommunications Center of the National Institute of Scientific Research (INRS-EMT), Canada. He is the founding director of the Resilient Cybersecurity Laboratory (RSEC) and a member of the INRS-UQO Joint Research Unit in Cybersecurity and Digital Trust.
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- [Contact](https://rseclab.com/contact/) - Research and Development At RSEC Laboratory, we believe in the power of open collaboration and cutting-edge research. RSEC Laboratory welcomes research collaborations and consultations in Artificial Intelligence (AI), Quantum AI and its applications in cybersecurity, Blockchain Technology and its applications, Enterprise Security Architecture, and the Security of Internet of Things (IoT). Developing intelligent and resilient systems. Protecting
- [About](https://rseclab.com/about/) - Towards Secure and Resilient B6G Networks Applied Artificial Intelligence for Cybersecurity Blockchain and its Applications Quantum Computing Internet Of Things About RSec Laboratory The Resilient cyberSECurity Research Laboratory (RSEC Lab) is dedicated to advancing cybersecurity resilience through cutting-edge research, innovation, and practical solutions. Our mission is to develop robust security frameworks, enhance threat detection methodologies,
- [Home2](https://rseclab.com/home2/) - ReSEC Lab Resilient cyberSECurity Laboratory Explore more Innovative Research with No Boundaries Pioneering AI and Cybersecurity Solutions At ReSec Laboratory, we believe in the power of open collaboration and cutting-edge research. Our projects are driven by innovation and are not confined by traditional boundaries, ensuring that all advancements are accessible and impactful. Developing tomorrow’s intelligent
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## Articles

- [Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework](https://rseclab.com/article/enhancing-spectral-efficiency-and-resilience-in-sagin-its-an-irs-assisted-semantic-offloading-framework/)
- [Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions](https://rseclab.com/article/federated-large-language-models-for-a-trustworthy-and-privacy-preserving-healthcare-applications-challenges-and-future-research-directions/)
- [Leveraging Large Language Models for Contextual Threat Hypothesis Generation in IoT Networks](https://rseclab.com/article/leveraging-large-language-models-for-contextual-threat-hypothesis-generation-in-iot-networks/)
- [Lightweight LLM Adaptation for Intrusion Detection via Token-Efficient Flow Representation](https://rseclab.com/article/lightweight-llm-adaptation-for-intrusion-detection-via-token-efficient-flow-representation/)
- [Quantum-Enhanced LSTM for Sequential Network Flow Analysis: A Hybrid Approach to DDoS Detection](https://rseclab.com/article/quantum-enhanced-lstm-for-sequential-network-flow-analysis-a-hybrid-approach-to-ddos-detection/)
- [Mitigating Gradient Inversion Attacks in Federated Learning over Tabular IoT Data](https://rseclab.com/article/mitigating-gradient-inversion-attacks-in-federated-learning-over-tabular-iot-data/)
- [QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks](https://rseclab.com/article/qsfl-id-quantum-split-federated-learning-for-intrusion-detection-in-iiot-networks/)
- [SelfAdv-DA: Unsupervised Domain Adaptation Framework for Robust Intrusion Detection in IoT Networks](https://rseclab.com/article/selfadv-da-unsupervised-domain-adaptation-framework-for-robust-intrusion-detection-in-iot-networks/)
- [Game-Theoretic Security Orchestration for Cross-RIC Policy Conflicts in O-RAN](https://rseclab.com/article/game-theoretic-security-orchestration-for-cross-ric-policy-conflicts-in-o-ran/)
- [Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework](https://rseclab.com/article/enhancing-spectral-efficiency-and-resilience-in-sagin-its-an-irs-assisted-semantic-offloading-framework-2/) - This paper proposes an intelligent reflecting surface (IRS)-assisted semantic offloading framework to improve spectral efficiency and resilience in satellite-air-ground integrated networks for intelligent transportation systems (SAGIN-ITS).
- [Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions](https://rseclab.com/article/federated-large-language-models-for-a-trustworthy-and-privacy-preserving-healthcare-applications-challenges-and-future-research-directions-2/) - This survey examines how federated large language models can support privacy-preserving, trustworthy healthcare applications, reviewing current approaches, open challenges, and future research directions.
- [Leveraging Large Language Models for Contextual Threat Hypothesis Generation in IoT Networks](https://rseclab.com/article/leveraging-large-language-models-for-contextual-threat-hypothesis-generation-in-iot-networks-2/) - This paper proposes a method that leverages large language models to generate context-aware threat hypotheses in IoT networks, helping analysts move faster from raw telemetry to actionable threat-hunting insight.
- [Lightweight LLM Adaptation for Intrusion Detection via Token-Efficient Flow Representation](https://rseclab.com/article/lightweight-llm-adaptation-for-intrusion-detection-via-token-efficient-flow-representation-2/) - This paper introduces a token-efficient representation of network flows that allows large language models to be adapted for intrusion detection on resource-constrained IoT hardware.
- [Quantum-Enhanced LSTM for Sequential Network Flow Analysis: A Hybrid Approach to DDoS Detection](https://rseclab.com/article/quantum-enhanced-lstm-for-sequential-network-flow-analysis-a-hybrid-approach-to-ddos-detection-2/) - This paper presents a hybrid quantum-classical LSTM architecture that integrates quantum circuits into the recurrent learning process to improve the detection of sequential DDoS attack patterns in network traffic.
- [Mitigating Gradient Inversion Attacks in Federated Learning over Tabular IoT Data](https://rseclab.com/article/mitigating-gradient-inversion-attacks-in-federated-learning-over-tabular-iot-data-2/) - This paper addresses privacy leakage in federated learning by proposing defenses against gradient inversion attacks targeting tabular IoT data.
- [QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks](https://rseclab.com/article/qsfl-id-quantum-split-federated-learning-for-intrusion-detection-in-iiot-networks-2/) - This paper introduces QSFL-ID, a quantum-split federated learning framework designed to improve intrusion detection accuracy and efficiency in industrial IoT networks.
- [SelfAdv-DA: Unsupervised Domain Adaptation Framework for Robust Intrusion Detection in IoT Networks](https://rseclab.com/article/selfadv-da-unsupervised-domain-adaptation-framework-for-robust-intrusion-detection-in-iot-networks-2/) - This paper proposes SelfAdv-DA, an unsupervised domain adaptation framework that improves the robustness of intrusion detection systems across varying IoT network conditions.
- [Game-Theoretic Security Orchestration for Cross-RIC Policy Conflicts in O-RAN](https://rseclab.com/article/game-theoretic-security-orchestration-for-cross-ric-policy-conflicts-in-o-ran-2/) - This paper introduces a game-theoretic approach for resolving security policy conflicts across RIC controllers in Open RAN deployments, improving coordinated security orchestration.
- [Integrating Zero Trust Architecture in O-RAN: A Comprehensive Survey and Analysis](https://rseclab.com/article/integrating-zero-trust-architecture-in-o-ran-a-comprehensive-survey-and-analysis/) - A comprehensive survey and analysis of integrating Zero Trust Architecture in Open Radio Access Networks (O-RAN), addressing security challenges and opportunities.
- [Scaling Text-Based Semantic Communication Systems With Heterogeneous Mixture of Experts](https://rseclab.com/article/scaling-text-based-semantic-communication-systems-with-heterogeneous-mixture-of-experts/) - Proposes scaling text-based semantic communication systems using heterogeneous Mixture of Experts for improved wireless communication efficiency.
- [Toward a Secure Zero-Touch Tactile Internet: Challenges and Opportunities](https://rseclab.com/article/toward-a-secure-zero-touch-tactile-internet-challenges-and-opportunities/) - Explores challenges and opportunities toward achieving a secure zero-touch Tactile Internet, addressing automation, security, and ultra-low latency requirements.
- [Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks](https://rseclab.com/article/enhancing-network-intrusion-detection-systems-a-multi-layer-ensemble-approach-to-mitigate-adversarial-attacks/) - Proposes a multi-layer ensemble approach to enhance network intrusion detection systems and mitigate adversarial attacks.
- [Graph Neural Network Framework for Advanced Persistent Threat Detection in IIoT Environments](https://rseclab.com/article/graph-neural-network-framework-for-advanced-persistent-threat-detection-in-iiot-environments/) - Proposes a Graph Neural Network framework for detecting Advanced Persistent Threats in Industrial Internet of Things environments.
- [Collaborative Learning for 6G Mobile Wireless Networks](https://rseclab.com/article/collaborative-learning-for-6g-mobile-wireless-networks/) - Collaborative Learning for 6G Mobile Wireless Networks gives a comprehensive introduction to the topic and its potential role in the development of 6G by explaining principles and presenting methods, algorithms, and uses cases. To achieve 6G’s vision of intelligent and autonomous networks capable of self-optimization, self-healing, and context-aware adaptation, there is a need to develop advanced
- [Split Federated Learning-Driven Resource-Efficient MEC Framework for UAV-based Networks](https://rseclab.com/article/split-federated-learning-driven-resource-efficient-mec-framework-for-uav-based-networks/) - Distributed collaborative machine learning techniques enable the training of intelligent models while preserving user data privacy. However, in reality, training a large-scale and intricate model on resource-constrained devices such as Unmanned Aerial Vehicles (UAVs) is unfeasible. In this context, lightweight and resource-efficient deep learning techniques are required. This work first suggests a new resource-aware distributed
- [Domain Adversarial Neural Networks with  Adversarial Robustness Evaluation for Intrusion  Detection Systems](https://rseclab.com/article/domain-adversarial-neural-networks-with-adversarial-robustness-evaluation-for-intrusion-detection-systems/)
- [Enhancing IIoT Security with Deep Reinforcement Learning for Intrusion Detection](https://rseclab.com/article/enhancing-iiot-security-with-deep-reinforcement-learning-for-intrusion-detection/) - A novel approach leveraging Deep Reinforcement Learning (DRL) for intrusion detection, combining supervised learning within a DRL framework to address these challenges. Despite the difficulties in designing an effective reward system, our method, tested on the IoT23 dataset, demonstrates superior performance.
- [Enhanced Adversarial Domain Adaptation for Intrusion Detection Systems](https://rseclab.com/article/enhanced-adversarial-domain-adaptation-for-intrusion-detection-systems/) - Improves cross-domain generalization of IDS models using enhanced adversarial domain adaptation under dataset shift; to appear.
- [When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network](https://rseclab.com/article/when-federated-learning-meets-knowledge-distillation-to-secure-consumer-edge-network/) - Combines TinyML-oriented knowledge distillation with federated learning to secure consumer edge networks, improving privacy and performance on constrained devices.
- [LLMs to Secure Consumer Networks: Open Problems and Future Directions](https://rseclab.com/article/llms-to-secure-consumer-networks-open-problems-and-future-directions/) - A forward-looking perspective on using large language models to secure consumer networks, covering attack surfaces, defenses, and an agenda for future research.
- [A Blockchain-Based Cross-Domain DDoS Mitigation in Consumer Networks](https://rseclab.com/article/a-blockchain-based-cross-domain-ddos-mitigation-in-consumer-networks/) - Introduces SecureShare, a digital-twin and blockchain-enabled framework for inter-domain collaboration to mitigate large-scale DDoS attacks across SDN-based domains.
- [MetaPower: Empowering Peer-to-Peer Energy Trading in the Metaverse With Digital Twins and Blockchain](https://rseclab.com/article/metapower-empowering-peer-to-peer-energy-trading-in-the-metaverse-with-digital-twins-and-blockchain/) - Presents MetaPower, a blockchain and digital-twin based framework for secure, scalable peer-to-peer energy trading in metaverse environments.
- [Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems](https://rseclab.com/article/advancing-robustness-and-privacy-in-federated-learning-for-secure-autonomous-vehicle-systems/) - SecureFL framework with FGSA attack detection, GNN-based reputation, and RSMA-driven scheduling to strengthen privacy and robustness for FL in AV systems.
- [Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain](https://rseclab.com/article/advancing-privacy-and-fairness-in-healthcare-using-federated-edge-learning-and-blockchain/) - SecureMed: a fairness-aware federated edge learning architecture with SMPC and blockchain reputation to protect IoMT data while balancing performance across institutions.
- [Vehicular Edge Computing: An Enhanced Vehicle Participant Selection System for Federated Learning](https://rseclab.com/article/vehicular-edge-computing-an-enhanced-vehicle-participant-selection-system-for-federated-learning/) - Proposes a tabu-search based vehicle selection system to optimize federated learning in vehicular edge computing with privacy and performance constraints.
- [A Blockchain-Enabled Multi-Layered Zero-Trust Security Framework for O-RAN](https://rseclab.com/article/a-blockchain-enabled-multi-layered-zero-trust-security-framework-for-o-ran/) - Introduces a zero-trust, blockchain-enabled multi-layer security framework for O-RAN with FL/IoT considerations and real-time constraints.
- [Securing O-RAN Equipment Using Blockchain-Based Supply Chain Verification](https://rseclab.com/article/securing-o-ran-equipment-using-blockchain-based-supply-chain-verification/) - A blockchain-backed supply-chain verification scheme to authenticate firmware and strengthen the security of O-RAN equipment.
- [An SDN-based Adaptive Ensemble Learning Framework for Intrusion Mitigation in Wireless Networks](https://rseclab.com/article/an-sdn-based-adaptive-ensemble-learning-framework-for-intrusion-mitigation-in-wireless-networks/) - AdaptiveBoost leverages SDN and ensemble learning to detect and mitigate jamming in WSNs, improving accuracy and dramatically reducing training time.
- [Blockchain-Based Federated Learning for Enhanced Cyber-Threats Detection in Connected Vehicles](https://rseclab.com/article/blockchain-based-federated-learning-for-enhanced-cyber-threats-detection-in-connected-vehicles/) - VFed-IDS: a decentralized SDN + blockchain + federated learning IDS for the Internet of Vehicles, achieving high accuracy while preserving privacy.
- [Federated learning in healthcare](https://rseclab.com/article/federated-learning-in-healthcare/) - A chapter discussing federated learning for healthcare applications—threats, privacy concerns, and practical deployment guidance.
- [Securing O-RAN with Zero Trust Architectureand Large Language Models](https://rseclab.com/article/securing-o-ran-with-zero-trust-architectureand-large-language-models/) - The Open Radio Access Network (O-RAN) architecture is critical for the development of 6G networks, offering flexibility and interoperability through disaggregated components. However, this openness exposes O-RAN to new security vulnerabilities, including unauthorized access, data breaches, and malicious xApp deployments. To address these challenges, we propose DistillORAN, a novel Zero-Trust architecture designed specifically for O-RAN.
- [Digital Forensics in Next-Generation Internet for Medical Things](https://rseclab.com/article/digital-forensics-in-next-generation-internet-for-medical-things/) - Digital Forensics in Next-Generation Internet for Medical Things
- [An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection](https://rseclab.com/article/an-in-depth-comparative-study-of-quantum-classical-encoding-methods-for-network-intrusion-detection/)
- [Securing the Metaverse: The Intersection of ML-Based Oracles and Blockchain Technology](https://rseclab.com/article/co-iot-a-collaborative-ddos-mitigation-scheme-in-iot-environment-based-on-blockchain-using-sdn-2/) - The integration of blockchain and machine learning (ML) in decentralized systems has revolutionized the operational potential of smart contracts, especially in dynamic and interactive environments such as the metaverse. Smart contracts leverage blockchain technology to enable secure, decentralized, and transparent automation of agreements without intermediaries. However, their reliability often depends on external data sources, known
- [A Novel Unsupervised Learning Method for Intrusion Detection in Software-Defined Networks](https://rseclab.com/article/a-novel-unsupervised-learning-method-for-intrusion-detection-in-software-defined-networks/) - This chapter presents a novel unsupervised learning method for intrusion detection in software-defined networks, exploring advanced techniques in computational intelligence for improving network security.
- [Cyber Threat Actors Review: Examining the Tactics and Motivations of Adversaries in the Cyber Landscape](https://rseclab.com/article/cyber-threat-actors-review-examining-the-tactics-and-motivations-of-adversaries-in-the-cyber-landscape/) - This chapter investigates the tactics and motivations of cyber threat actors, including nation-states, criminal organizations, and hacktivists, and analyzes their methods such as social engineering, malware, and ransomware.
- [Foundations models in cybersecurity: A comprehensive review and future direction](https://rseclab.com/article/foundations-models-in-cybersecurity-a-comprehensive-review-and-future-direction/)
- [Zero Trust Security Architecture for 6G Open Radio Access Networks (ORAN)](https://rseclab.com/article/zero-trust-security-architecture-for-6g-open-radio-access-networks-oran/)
- [Blockchain integration with IoT](https://rseclab.com/article/la-blockchain-pour-la-securite-de-linternet-des-objets-ido/) - This thesis explores how blockchain technology can be applied to enhance the security of the Internet of Things (IoT).
- [A Lightweight Machine Learning Model for IoT Security](https://rseclab.com/article/a-lightweight-cybersecurity-model-for-iot-and-iot-networks/) - This master thesis presents a lightweight cybersecurity model specifically designed for IoT and IoT networks, addressing vulnerabilities and offering a scalable solution.
- [CYBER SECURITY FOR NEXT-GENERATION COMPUTING TECHNOLOGIES](https://rseclab.com/article/cyber-security-for-next-generation-computing-technologies/) - This book sheds light on the cyber security challenges associated with next-generation computing technologies, emphasizing threats to individuals, businesses, and nations in an increasingly interconnected world. It covers key topics related to cybersecurity, data security, and the convergence of AI/ML in cybersecurity.
- [Advancing Security and Trust in WSNs: A Federated Multi-Agent Deep Reinforcement Learning Approach](https://rseclab.com/article/advancing-security-and-trust-in-wsns-a-federated-multi-agent-deep-reinforcement-learning-approach/) - This paper presents a federated multi-agent deep reinforcement learning approach to enhance security and trust in Wireless Sensor Networks (WSNs).
- [Reputation-Aware Scheduling for Secure Internet of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach](https://rseclab.com/article/reputation-aware-scheduling-for-secure-internet-of-drones-a-federated-multi-agent-deep-reinforcement-learning-approach/) - This paper presents a federated multi-agent deep reinforcement learning approach to secure the Internet of Drones through reputation-aware scheduling.
- [Method for Processing a Data Packet and Associated Device, Switching Equipment and Computer Program](https://rseclab.com/article/method-for-processing-a-data-packet-and-associated-device-switching-equipment-and-computer-program/) - This patent describes a method for processing a data packet, along with the associated device, switching equipment, and computer program designed to improve network data handling efficiency.
- [Towards a Secure and Scalable Access Control System Using Blockchain](https://rseclab.com/article/towards-a-secure-and-scalable-access-control-system-using-blockchain/) - This conference paper presents a blockchain-based approach for secure and scalable access control systems.
- [Secure and Efficient Federated Learning for Robust Intrusion Detection in IoT Networks](https://rseclab.com/article/secure-and-efficient-federated-learning-for-robust-intrusion-detection-in-iot-networks/) - This conference paper presents a federated learning-based approach for enhancing intrusion detection in IoT networks.
- [Securing Federated Learning through Blockchain and Explainable AI for Robust Intrusion Detection in IoT Networks](https://rseclab.com/article/securing-federated-learning-through-blockchain-and-explainable-ai-for-robust-intrusion-detection-in-iot-networks/) - This paper explores the integration of blockchain and explainable AI to enhance federated learning for IoT network security.
- [Cost-efficient Federated Reinforcement Learning-Based Network Routing for Wireless Networks](https://rseclab.com/article/cost-efficient-federated-reinforcement-learning-based-network-routing-for-wireless-networks/) - This conference paper presents a cost-efficient federated reinforcement learning approach to optimize network routing in wireless networks.
- [Blockchain Meets O-RAN: A Decentralized Zero-Trust Framework for Secure and Resilient O-RAN in 6G and Beyond](https://rseclab.com/article/blockchain-meets-o-ran-a-decentralized-zero-trust-framework-for-secure-and-resilient-o-ran-in-6g-and-beyond/) - This paper discusses a decentralized zero-trust framework for ensuring secure and resilient O-RAN in 6G and beyond using blockchain technology.
- [Advancing Security and Efficiency in Federated Learning Service Aggregation for Wireless Networks](https://rseclab.com/article/advancing-security-and-efficiency-in-federated-learning-service-aggregation-for-wireless-networks/) - This paper focuses on enhancing the security and efficiency of federated learning service aggregation in wireless networks.
- [Ensemble Learning for Intrusion Detection in SDN-Based Zero Touch Smart Grid Systems](https://rseclab.com/article/ensemble-learning-for-intrusion-detection-in-sdn-based-zero-touch-smart-grid-systems/) - This paper discusses the application of ensemble learning techniques for improving intrusion detection in Software-Defined Networking (SDN)-based smart grid systems.
- [A Low-Latency Fog-based Framework to Secure IoT Applications using Collaborative Federated Learning](https://rseclab.com/article/a-low-latency-fog-based-framework-to-secure-iot-applications-using-collaborative-federated-learning/) - This paper introduces a low-latency fog-based framework that enhances security for IoT applications through collaborative federated learning.
- [A Hierarchical Fog Computing Framework for Network Attack Detection in SDN](https://rseclab.com/article/a-hierarchical-fog-computing-framework-for-network-attack-detection-in-sdn/) - This paper introduces a hierarchical fog computing framework designed to enhance network attack detection within Software-Defined Networks (SDN).
- [A Novel Machine Learning Framework for Advanced Attack Detection using SDN](https://rseclab.com/article/a-novel-machine-learning-framework-for-advanced-attack-detection-using-sdn/) - This paper presents a novel machine learning framework designed for advanced attack detection in Software Defined Networks (SDN).
- [Blockchain-based Reverse Auction for V2V charging in smart grid environment](https://rseclab.com/article/blockchain-based-reverse-auction-for-v2v-charging-in-smart-grid-environment/) - This paper discusses a blockchain-based reverse auction system for vehicle-to-vehicle (V2V) charging within a smart grid environment.
- [Blockchain Meets AMI: Towards Secure Advanced Metering Infrastructures](https://rseclab.com/article/blockchain-meets-ami-towards-secure-advanced-metering-infrastructures/) - This paper explores the intersection of blockchain technology with Advanced Metering Infrastructures (AMI), proposing solutions for enhanced security in smart grid environments.
- [BrainChain - A Machine learning Approach for protecting Blockchain applications using SDN](https://rseclab.com/article/brainchain-a-machine-learning-approach-for-protecting-blockchain-applications-using-sdn/) - This paper presents a machine learning approach named BrainChain for enhancing the security of blockchain applications by utilizing Software Defined Networks (SDN).
- [Co-IoT: A Collaborative DDoS Mitigation Scheme in IoT Environment Based on Blockchain Using SDN](https://rseclab.com/article/co-iot-a-collaborative-ddos-mitigation-scheme-in-iot-environment-based-on-blockchain-using-sdn/) - This paper presents Co-IoT, a collaborative approach leveraging blockchain and SDN for mitigating DDoS attacks in IoT environments.
- [ChainSecure - A Scalable and Proactive Solution for Protecting Blockchain Applications Using SDN](https://rseclab.com/article/chainsecure-a-scalable-and-proactive-solution-for-protecting-blockchain-applications-using-sdn/) - This paper introduces ChainSecure, a scalable and proactive approach to securing blockchain applications through the use of Software Defined Networking (SDN).
- [A Privacy-Preserving Framework for Efficient Network Intrusion Detection in Consumer Network Using Quantum Federated Learning](https://rseclab.com/article/a-privacy-preserving-framework-for-efficient-network-intrusion-detection-in-consumer-network-using-quantum-federated-learning/) - This paper introduces a Quantum Federated Learning IDS (QFL-IDS) framework, merging Quantum Computing and Federated Learning to provide an efficient and privacy-preserving solution for network intrusion detection in consumer networks.
- [Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning](https://rseclab.com/article/securing-iiot-applications-in-6g-and-beyond-using-adaptive-ensemble-learning-and-zero-touch-multi-resource-provisioning/) - This article discusses securing Industrial Internet of Things (IIoT) applications in 6G using ensemble learning.
- [A Privacy-Preserving Collaborative Jamming Attacks Detection Framework Using Federated Learning](https://rseclab.com/article/a-privacy-preserving-collaborative-jamming-attacks-detection-framework-using-federated-learning/) - This study presents a framework for detecting jamming attacks using federated learning in IoT environments.
- [Blockchain-Enabled Federated Learning for Enhanced Collaborative Intrusion Detection in Vehicular Edge Computing](https://rseclab.com/article/blockchain-enabled-federated-learning-for-enhanced-collaborative-intrusion-detection-in-vehicular-edge-computing/) - This paper explores the use of blockchain and federated learning for intrusion detection in vehicular edge computing.
- [Federated Deep Reinforcement Learning for Efficient Jamming Attack Mitigation in O-RAN](https://rseclab.com/article/federated-deep-reinforcement-learning-for-efficient-jamming-attack-mitigation-in-o-ran/) - This article explores the use of federated deep reinforcement learning to mitigate jamming attacks in O-RAN environments.
- [Next-power: Next-generation framework for secure and sustainable energy trading in the metaverse](https://rseclab.com/article/next-power-next-generation-framework-for-secure-and-sustainable-energy-trading-in-the-metaverse/) - This article introduces Next-power, a framework for secure and sustainable energy trading in the metaverse.
- [MiTFed: A Privacy-Preserving Collaborative Network Attack Mitigation Framework Based on Federated Learning Using SDN and Blockchain](https://rseclab.com/article/mitfed-a-privacy-preserving-collaborative-network-attack-mitigation-framework-based-on-federated-learning-using-sdn-and-blockchain/) - This article presents MiTFed, a framework for collaborative network attack mitigation leveraging federated learning, SDN, and blockchain.
- [When Collaborative Federated Learning Meets Blockchain to Preserve Privacy in Healthcare](https://rseclab.com/article/when-collaborative-federated-learning-meets-blockchain-to-preserve-privacy-in-healthcare/) - This article discusses the integration of federated learning with blockchain to enhance privacy in healthcare systems.
- [A Novel IoT-Based Explainable Deep Learning Framework for Intrusion Detection Systems](https://rseclab.com/article/a-novel-iot-based-explainable-deep-learning-framework-for-intrusion-detection-systems/) - This article introduces an IoT-based explainable deep learning framework aimed at improving intrusion detection systems.
- [A MEC-Based Architecture to Secure IoT Applications using Federated Deep Learning](https://rseclab.com/article/a-mec-based-architecture-to-secure-iot-applications-using-federated-deep-learning/) - This article explores a MEC-based architecture for securing IoT applications through federated deep learning techniques.
- [When Federated Learning Meets Game Theory: A Cooperative Framework to Secure IIoT Applications on Edge Computing](https://rseclab.com/article/when-federated-learning-meets-game-theory-a-cooperative-framework-to-secure-iiot-applications-on-edge-computing/) - This paper introduces a cooperative framework integrating federated learning and game theory to enhance the security of IIoT applications on edge computing.
- [Bringing Intelligence to Software Defined Networks: Mitigating DDoS Attacks](https://rseclab.com/article/bringing-intelligence-to-software-defined-networks-mitigating-ddos-attacks/) - This article explores methods for integrating intelligence into Software Defined Networks (SDNs) to effectively mitigate Distributed Denial-of-Service (DDoS) attacks.
- [Security Enforcement through Software Defined Networks (SDN). (Renforcement de la sécurité à travers les réseaux programmables)](https://rseclab.com/article/security-enforcement-through-software-defined-networks-sdn-renforcement-de-la-securite-a-travers-les-reseaux-programmables/) - This thesis explores security enforcement mechanisms through Software Defined Networks (SDN), focusing on programmable networks.
- ["Why Should I Trust Your IDS?": An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks](https://rseclab.com/article/why-should-i-trust-your-ids-an-explainable-deep-learning-framework-for-intrusion-detection-systems-in-internet-of-things-networks/) - This article proposes an explainable deep learning framework for improving the trustworthiness of intrusion detection systems in IoT networks.
- [Trust Management in Vehicular Ad-Hoc Networks: Extensive Survey](https://rseclab.com/article/trust-management-in-vehicular-ad-hoc-networks-extensive-survey/) - This article provides an extensive survey on trust management in vehicular ad-hoc networks.
- [Cochain-SC: An Intra- and Inter-Domain DDoS Mitigation Scheme Based on Blockchain Using SDN and Smart Contract](https://rseclab.com/article/cochain-sc-an-intra-and-inter-domain-ddos-mitigation-scheme-based-on-blockchain-using-sdn-and-smart-contract/) - This article proposes Cochain-SC, a blockchain-based scheme utilizing Software Defined Networks (SDN) and smart contracts for intra- and inter-domain DDoS mitigation.

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