QCAE-QOC-SVM: A hybrid quantum machine learning model for DoS and Fuzzy attack detection on autonomous vehicle CAN bus

In this study, we introduce a hybrid quantum machine learning method to identify Normal signal, DoS, and Fuzzy attacks on the CAN bus utilized in autonomous vehicles. Our approach is a combination of a Quantum Convolutional Autoencoder (QCAE) and a Quantum Orthogonal Classifier based on Support Vect...

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Vydané v:	MethodsX Ročník 15; s. 103471
Hlavní autori:	R, Meghana, Ramesha, Sowmyashree Sakrepatna, Mukhopadhyay, Adwitiya
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.12.2025 Elsevier
Predmet:	A hybrid of Quantum Convolutional Autoencoder and Quantum Orthogonal Classifier with SVM Anomaly recognition system Cybersecurity in autonomous cars Hybrid classification techniques Quantum feature optimization A hybrid of Quantum Convolutional Autoencoder and Quantum Orthogonal Classifier with SVM Quantum feature optimization Anomaly recognition system Cybersecurity in autonomous cars Hybrid classification techniques
ISSN:	2215-0161, 2215-0161
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Shrnutí:	In this study, we introduce a hybrid quantum machine learning method to identify Normal signal, DoS, and Fuzzy attacks on the CAN bus utilized in autonomous vehicles. Our approach is a combination of a Quantum Convolutional Autoencoder (QCAE) and a Quantum Orthogonal Classifier based on Support Vector Machines (QOC-SVM). The method effectively extracts patterns from CAN bus traffic with the help of quantum-powered classification for accurate anomaly detection. The model was assessed using a public and custom dataset of 300,000 instances generated through the CARLA simulator and was run on a high-performance computing facility. Results from the experiments show that the QCAE-QOC-SVM model performs better than conventional machine learning (ML), deep learning (DL), and other quantum machine learning (QML) models with an F1 score of 99.43 % when the batches-to-batch size ratio is 7741:31. These findings indicate the possibility of quantum machine learning to significantly improve strong defense mechanisms against cyber-attacks for intelligent transportation systems. The high accuracy and resistance of the model proposed indicate good prospects for real-time autonomous vehicle security, with enhanced detection of sophisticated attack patterns. Our contribution is substantial in the creation of future-proof cybersecurity solutions for the fast-changing autonomous vehicle technology and intelligent transportation system domain.•Introduction of a hybrid quantum machine learning model for attack detection on autonomous vehicle CAN buses.•Demonstrated superior performance with an F1 score of 99.43 % compared to traditional ML, DL, and QML models.•Showed the potential of quantum machine learning in strengthening defense systems for intelligent transportation networks. [Display omitted]
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2215-0161 2215-0161
DOI:	10.1016/j.mex.2025.103471