@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}

}