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

arXiv: https://arxiv.org/abs/2110.07467 · HTML: https://mlsanity.com/paper/2110.07467/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [TensorFlow Quantum: A Software Framework for Quantum Machine Learning](https://mlsanity.com/paper/2003.02989/) (2020)
- [Quantum Computing Methods for Supervised Learning](https://mlsanity.com/paper/2006.12025/) (2020)

## Built on by (most cited first, 4 of 4)

- [Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2412.02539/) (2024)
- [Dual-Stream Alignment for Action Segmentation](https://mlsanity.com/paper/2510.07652/) (2025)
- [Computing the gradients with respect to all parameters of a quantum neural network using a single circuit](https://mlsanity.com/paper/2307.08167/) (2023)
- [Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio](https://mlsanity.com/paper/2403.02080/) (2024)

## Abstract (arXiv)

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%)