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Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection

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posted on 08.12.2021, 16:37 by Mhafuzul islamMhafuzul islam, Mashrur ChowdhuryMashrur Chowdhury, Sakib Khan, Zadid Khan
A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers. In a cloud-supported cyber-physical system environment, running a machine learning application in quantum computers is often difficult, due to the existing limitations of the current quantum devices. However, with the combination of quantum-classical neural networks (NN), complex and high-dimensional features can be extracted by the classical NN to a reduced but more informative feature space to be processed by the existing quantum computers. In this study, we develop a hybrid quantum-classical NN to detect an amplitude shift cyber-attack on an in-vehicle control area network (CAN) dataset. We show that using the hybrid quantum-classical NN, it is possible to achieve an attack detection accuracy of 94%, which is higher than a Long short-term memory (LSTM) NN (87%) or quantum NN alone (62%)

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Email Address of Submitting Author

mdmhafi@g.clemson.edu

ORCID of Submitting Author

0000-0002-2890-3741

Submitting Author's Institution

Clemson University

Submitting Author's Country

United States of America

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