Network Attack Traffic Detection With Hybrid Quantum-Enhanced Convolution Neural Network
Zihao Wang, Kar Wai Fok, Vrizlynn L. L. Thing
TL;DR
The paper investigates whether hybrid quantum-classical convolutional networks can improve malicious network traffic detection, with a focus on unseen attacks. Five QCNN architectures employing different quantum embeddings and layer placements are designed and benchmarked against a classical CNN on the 5G-NIDD dataset. Results indicate QCNN variants generally outperform the classical baseline in open-set/unknown-attack scenarios, though performance depends on embedding strategy and data size, with stability concerns for some configurations. The study demonstrates the potential of quantum-enhanced classifiers for intrusion detection and highlights areas for future improvement, including initialization, noise handling, and scalable quantum-layer design.
Abstract
The emerging paradigm of Quantum Machine Learning (QML) combines features of quantum computing and machine learning (ML). QML enables the generation and recognition of statistical data patterns that classical computers and classical ML methods struggle to effectively execute. QML utilizes quantum systems to enhance algorithmic computation speed and real-time data processing capabilities, making it one of the most promising tools in the field of ML. Quantum superposition and entanglement features also hold the promise to potentially expand the potential feature representation capabilities of ML. Therefore, in this study, we explore how quantum computing affects ML and whether it can further improve the detection performance on network traffic detection, especially on unseen attacks which are types of malicious traffic that do not exist in the ML training dataset. Classical ML models often perform poorly in detecting these unseen attacks because they have not been trained on such traffic. Hence, this paper focuses on designing and proposing novel hybrid structures of Quantum Convolutional Neural Network (QCNN) to achieve the detection of malicious traffic. The detection performance, generalization, and robustness of the QML solutions are evaluated and compared with classical ML running on classical computers. The emphasis lies in assessing whether the QML-based malicious traffic detection outperforms classical solutions. Based on experiment results, QCNN models demonstrated superior performance compared to classical ML approaches on unseen attack detection.