Publication

Abstract : 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.