@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}

}