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.
Kadi, A.; Selamnia, A.; Houda, Z. A. E.; Moudoud, H.; Brik, B.; Khoukhi, L.
An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection Article de journal
Dans: IEEE Open Journal of the Communications Society, vol. 6, p. 1129–1148, 2025, ISSN: 2644125X (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Cyber attacks, Embeddings, Encoding methods, Encoding techniques, Encodings, Intrusion Detection, intrusion detection system, Intrusion Detection Systems, Machine-learning, Network embeddings, Network intrusion, Quantum cryptography, Quantum efficiency, Quantum electronics, Quantum machine learning, Quantum machines, Quantum-classical, Quantum-classical encoding, Zero-day attack
@article{kadiInDepthComparativeStudy2025,
title = {An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection},
author = {A. Kadi and A. Selamnia and Z. A. E. Houda and H. Moudoud and B. Brik and L. Khoukhi},
url = {https://www.scopus.com/pages/publications/85217024576?origin=resultslist},
doi = {10.1109/OJCOMS.2025.3537957},
issn = {2644125X (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Open Journal of the Communications Society},
volume = {6},
pages = {1129–1148},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In today's rapidly evolving cyber landscape, the growing sophistication of attacks, including the rise of zero-day exploits, poses critical challenges for network intrusion detection. Traditional Intrusion Detection Systems (IDSs) often struggle with the complexity and high dimensionality of modern cyber threats. Quantum Machine Learning (QML) seamlessly integrates the computational power of quantum computing with the adaptability of machine learning, offering an innovative approach to solving intricate and high-dimensional challenges. A key factor in QML's performance is the method used to encode classical data into quantum states, as it defines how data is represented and processed in quantum circuits. QML offers promising advances for IDS, particularly through hybrid quantum-classical models. This study presents an in-depth comparative analysis of quantum-classical data encoding techniques for QML-based IDS. To the best of our knowledge, this is the first study to comprehensively evaluate the performance impact of different quantum encoding methods and provide a thorough evaluation of their impacts on the overall model performances. To achieve this, we first present a comprehensive evaluation of quantum and classical data encoding techniques, focusing on four key encoding techniques namely, Amplitude Embedding, Angle Embedding, Instantaneous Quantum Polynomial (IQP) Encoding, and Quantum Approximate Optimization Algorithm (QAOA) Embedding. Then, we develop a hybrid quantum-classical QML model to analyze how each encoding affects classification performance for malicious traffic. Finally, we conduct extensive experiments using two well-known, real-world network attack datasets to assess the accuracy and efficiency of each encoding approach. Our obtained results show notable differences in classification accuracy, underscoring the importance of encoding choice in optimizing QML-based IDS. This study aims to advance the application of quantum methodologies in network security by identifying effective encoding strategies for intrusion detection. © 2025 IEEE.},
keywords = {Adversarial machine learning, Cyber attacks, Embeddings, Encoding methods, Encoding techniques, Encodings, Intrusion Detection, intrusion detection system, Intrusion Detection Systems, Machine-learning, Network embeddings, Network intrusion, Quantum cryptography, Quantum efficiency, Quantum electronics, Quantum machine learning, Quantum machines, Quantum-classical, Quantum-classical encoding, Zero-day attack},
pubstate = {published},
tppubtype = {article}
}
In today's rapidly evolving cyber landscape, the growing sophistication of attacks, including the rise of zero-day exploits, poses critical challenges for network intrusion detection. Traditional Intrusion Detection Systems (IDSs) often struggle with the complexity and high dimensionality of modern cyber threats. Quantum Machine Learning (QML) seamlessly integrates the computational power of quantum computing with the adaptability of machine learning, offering an innovative approach to solving intricate and high-dimensional challenges. A key factor in QML's performance is the method used to encode classical data into quantum states, as it defines how data is represented and processed in quantum circuits. QML offers promising advances for IDS, particularly through hybrid quantum-classical models. This study presents an in-depth comparative analysis of quantum-classical data encoding techniques for QML-based IDS. To the best of our knowledge, this is the first study to comprehensively evaluate the performance impact of different quantum encoding methods and provide a thorough evaluation of their impacts on the overall model performances. To achieve this, we first present a comprehensive evaluation of quantum and classical data encoding techniques, focusing on four key encoding techniques namely, Amplitude Embedding, Angle Embedding, Instantaneous Quantum Polynomial (IQP) Encoding, and Quantum Approximate Optimization Algorithm (QAOA) Embedding. Then, we develop a hybrid quantum-classical QML model to analyze how each encoding affects classification performance for malicious traffic. Finally, we conduct extensive experiments using two well-known, real-world network attack datasets to assess the accuracy and efficiency of each encoding approach. Our obtained results show notable differences in classification accuracy, underscoring the importance of encoding choice in optimizing QML-based IDS. This study aims to advance the application of quantum methodologies in network security by identifying effective encoding strategies for intrusion detection. © 2025 IEEE.