Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Kadi, A.; Moudoud, H.; Khoukhi, L.; Houda, Z. A. El
Quantum-Enhanced LSTM for Sequential Network Flow Analysis: A Hybrid Approach to DDoS Detection Article d'actes
Dans: Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC, p. 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).
Résumé | Liens | BibTeX | Étiquettes: 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.
2.
Moudoud, H.; Houda, Z. A. El; Brik, B.
A Blockchain-Based Cross-Domain DDoS Mitigation in Consumer Networks Article de journal
Dans: IEEE Transactions on Consumer Electronics, vol. 71, no 2, p. 7095–7104, 2025, ISSN: 00983063 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Block-chain, Blockchain, Consumer network, Denial-of-service attack, digital twins, Distributed denial of service, Edge Computing, Fuzzy logic, Fuzzy-Logic, Inter-domain, Internet service providers, Network function virtualization, Network functions, NFV, Platform as a Service (PaaS), Resources sharing, SDN, Software-defined networkings, Virtualizations
@article{moudoudBlockchainBasedCrossDomainDDoS2025,
title = {A Blockchain-Based Cross-Domain DDoS Mitigation in Consumer Networks},
author = {H. Moudoud and Z. A. El Houda and B. Brik},
url = {https://www.scopus.com/pages/publications/105002613162?origin=resultslist},
doi = {10.1109/TCE.2025.3559451},
issn = {00983063 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {71},
number = {2},
pages = {7095–7104},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Distributed Denial of Service (DDoS) attacks pose significant threats to the availability and security of consumer networks and Internet service providers (ISPs). This is a significant concern due to the potential vulnerabilities and security risks associated with the rapid increase in the number of insecure Internet of Things (IoT) devices. Adopting an inter-domain DDoS collaboration strategy is a promising solution to address this issue. However, manual configuration and management of resources across multiple domains can be time-consuming, error-prone, and inefficient. Moreover, the existing inter-domain DDoS mitigation mechanisms (i.e., Cooperative Defense mechanisms) are facing obstacles due to the lack of incentives for cooperation, low flexibility, and high cost. Most importantly, many of them are centralized, which risks single points of failure, hampering collaboration and resource sharing among Autonomous Systems (ASs). The new emerging techniques, such as Digital-Twin (DT) empowered by Network Function Virtualization (NFV), Software-Defined Networking (SDN), and Blockchain introduce new opportunities for efficient and flexible inter-domain DDoS collaboration i.e., resources sharing among multiple SDN-based domains. In this context, we propose SecureShare, a novel digital twin-enabled inter-domain DDoS mitigation framework that allows for an efficient, fair, and secure dynamic resource-sharing among SDN-based domains to deal with large-scale DDoS attacks through resource sharing. The deployment of SecureShare is executed within Ethereum’s test network, Sepolia. Furthermore, we performed extensive experiments employing Microsoft Azure Digital Twins (ADT), a platform-as-a-service tool for generating twin graphs of physical objects. The experimental results show that SecureShare achieves promising results in terms of efficiency, security, and flexibility. © 1975-2011 IEEE.},
keywords = {Block-chain, Blockchain, Consumer network, Denial-of-service attack, digital twins, Distributed denial of service, Edge Computing, Fuzzy logic, Fuzzy-Logic, Inter-domain, Internet service providers, Network function virtualization, Network functions, NFV, Platform as a Service (PaaS), Resources sharing, SDN, Software-defined networkings, Virtualizations},
pubstate = {published},
tppubtype = {article}
}
Distributed Denial of Service (DDoS) attacks pose significant threats to the availability and security of consumer networks and Internet service providers (ISPs). This is a significant concern due to the potential vulnerabilities and security risks associated with the rapid increase in the number of insecure Internet of Things (IoT) devices. Adopting an inter-domain DDoS collaboration strategy is a promising solution to address this issue. However, manual configuration and management of resources across multiple domains can be time-consuming, error-prone, and inefficient. Moreover, the existing inter-domain DDoS mitigation mechanisms (i.e., Cooperative Defense mechanisms) are facing obstacles due to the lack of incentives for cooperation, low flexibility, and high cost. Most importantly, many of them are centralized, which risks single points of failure, hampering collaboration and resource sharing among Autonomous Systems (ASs). The new emerging techniques, such as Digital-Twin (DT) empowered by Network Function Virtualization (NFV), Software-Defined Networking (SDN), and Blockchain introduce new opportunities for efficient and flexible inter-domain DDoS collaboration i.e., resources sharing among multiple SDN-based domains. In this context, we propose SecureShare, a novel digital twin-enabled inter-domain DDoS mitigation framework that allows for an efficient, fair, and secure dynamic resource-sharing among SDN-based domains to deal with large-scale DDoS attacks through resource sharing. The deployment of SecureShare is executed within Ethereum’s test network, Sepolia. Furthermore, we performed extensive experiments employing Microsoft Azure Digital Twins (ADT), a platform-as-a-service tool for generating twin graphs of physical objects. The experimental results show that SecureShare achieves promising results in terms of efficiency, security, and flexibility. © 1975-2011 IEEE.