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Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection
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  • Mhafuzul islam ,
  • Mashrur Chowdhury ,
  • Sakib Khan ,
  • Zadid Khan
Mhafuzul islam
Clemson University, Clemson University

Corresponding Author:[email protected]

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Mashrur Chowdhury
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Sakib Khan
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Zadid Khan
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Abstract

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%)
Apr 2022Published in IEEE Sensors Letters volume 6 issue 4 on pages 1-4. 10.1109/LSENS.2022.3153931