Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection
This addresses cyberattack detection in cloud-supported in-vehicle systems, though it appears incremental as it builds on existing quantum-classical hybrid approaches.
The paper tackled the problem of detecting amplitude shift cyber-attacks on in-vehicle CAN networks by developing a hybrid quantum-classical neural network, achieving a detection accuracy of 94%, which outperformed LSTM (87%) and quantum-only (62%) methods.
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%)