Network attack detection scheme based on variational quantum neural network

Cheng, Gong; Guan, Weiqi; Gani, Abdullah; Qi, Han

doi:10.1007/s11227-022-04542-z

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…Using the erroneous quantum hardware, they measure a prediction error of 11.96 %, compared to the TFQ simulator. Thus, their approach suffers from the system noise of the quantum computer [16].…”

Section: Related Workmentioning

confidence: 99%

A Case Study for Cyber-Attack Detection using Quantum Variational Circuits

Moll

Kunczik

2023

Preprint

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Identification of malicious attacks in network traffic is one of many important Big Data problems that have been approached from different directions, including machine learning. In this paper, it is used as an example for investigating the applicability of quantum machine learning to such problems. Particular focus is kept on the NISQ era, in which available computer sizes are typically fairly small. A new approach is introduced that allows quantum variational circuits to be split between different computers without the typical overhead cost of cutting and knitting techniques. It is compared to classical and established quantum approaches on a real-world data set.

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Section: Related Workmentioning

confidence: 99%

A Case Study for Cyber-Attack Detection using Quantum Variational Circuits

Moll

Kunczik

2023

Preprint

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“…Recent studies [16] [17] [18] [19] have substantiated the efficacy of Quantum Neural Network (QNN) and Quantum Support Vector Machines (QSVM) in classifying network activities. These findings have demonstrated exceptionally high detection rates in noise-less simulation, showcasing the promise of quantum approaches in the field of network security and intrusion detection; however, none of them have shown good classification performance on a physical quantum computer.…”

Section: Introductionmentioning

confidence: 99%

Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers

Kukliansky,

Orescanin,

Bollmann

et al. 2024

IEEE Trans. Quantum Eng.

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The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in Quantum Machine Learning (QML) offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum (NISQ) era machines. In this study, we explore Quantum Neural Networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. Our approach includes efficient classical feature encoding, QNN classifier selection, and performance tuning leveraging current quantum computational power. This study culminates in an optimized multi-layered QNN architecture for network intrusion detection. A small version of the proposed architecture was implemented on IonQ's Aria-1 quantum computer, achieving a notable 0.86 F1 score using the NF-UNSW-NB15 dataset. Additionally, we introduce a novel metric, certainty factor, laying the foundation for future integration of uncertainty measures in quantum classification outputs. Moreover, this factor is used to predict the noise susceptibility of our quantum binary classification system.

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“…Differing from previous works, we propose in this paper a novel QNN model based on quantum convolution. Quantum convolutional neural networks (QCNNs) were first introduced by Cong et al [39], which then triggered a series of research efforts on QCNNs [40][41][42][43][44][45][46][47][48][49][50][51][52]. However, most of these studies are concentrated on the application of QCNNs in the field of computer vision, with very limited exploration in the NLP domain.…”

Section: Introductionmentioning

confidence: 99%