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 developed a hybrid quantum-classical NN to detect an amplitude shift cyberattack on an in-vehicle controller area network dataset. We showed that by 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 NN (88%) or quantum NN alone (62%).