Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection
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%)
Cite
@article{arxiv.2110.07467,
title = {Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection},
author = {Mhafuzul Islam and Mashrur Chowdhury and Zadid Khan and Sakib Mahmud Khan},
journal= {arXiv preprint arXiv:2110.07467},
year = {2021}
}
Comments
4 pages, 3 figures