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

TL;DR

This paper proposes a hybrid quantum-classical neural network to detect cyberattacks in in-vehicle networks, achieving higher accuracy than classical or quantum models alone, despite current quantum device limitations.

Contribution

It introduces a novel hybrid quantum-classical neural network architecture for cyberattack detection in vehicle networks, demonstrating improved accuracy over existing models.

Findings

Hybrid quantum-classical NN achieves 94% accuracy.

Outperforms classical LSTM and quantum NN alone.

Effective in cloud-supported in-vehicle cybersecurity.

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%)