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

• URL: http://arxiv.org/abs/2110.07467v1
• Date: Thu, 14 Oct 2021 15:40:33 GMT
• Title: Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection
• Authors: Mhafuzul Islam, Mashrur Chowdhury, Zadid Khan, Sakib Mahmud Khan
• Abstract summary: In a cloud-supported cyber-physical system environment, running a machine learning application in quantum computers is often difficult. We show that using the hybrid quantum classical NN, it is possible to achieve an attack detection accuracy of 94%.
• Score: 3.09487092349687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%)

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