A Novel Hybrid Quantum-Classical Framework for an In-vehicle Controller Area Network Intrusion Detection

In-vehicle controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based communication nature. In this study, we developed a hybrid quantum-classical CAN intrusion detection framework using a classical neural network (NN) and a quantum restricted Boltzmann machine (...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Salek, M Sabbir, Biswas, Pronab Kumar, Pollard, Jacquan, Hales, Jordyn, Shen, Zecheng, Dixit, Vivek, Chowdhury, Mashrur, Khan, Sakib Mahmud, Wang, Yao
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Computer hacking
Controller area network
Controllers
Cyberattack detection
Datasets
Feature extraction
Fuzzy sets
Generative adversarial networks
Generative artificial intelligence
Hybrid power systems
Image classification
Image contrast
Image reconstruction
In vehicle
Intrusion detection
Intrusion detection systems
Messages
Neural networks
Performance evaluation
Quantum artificial intelligence
Restricted Boltzmann machine
Training
Wireless communications
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Summary:In-vehicle controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based communication nature. In this study, we developed a hybrid quantum-classical CAN intrusion detection framework using a classical neural network (NN) and a quantum restricted Boltzmann machine (RBM). The classical NN is dedicated for feature extraction from CAN images generated from a vehicle's CAN bus data, while the quantum RBM is dedicated for CAN image reconstruction for a classification-based intrusion detection. To evaluate the performance of the hybrid quantum-classical CAN intrusion detection framework, we used a real-world CAN fuzzy attack dataset to create three separate attack datasets, where each dataset represents a unique set of features related to the vehicle. We compared the performance of our hybrid framework to a similar but classical-only framework. Our analyses showed that the hybrid framework performs better in CAN intrusion detection compared to the classical-only framework. For the three datasets considered in this study, the best models in the hybrid framework achieved 97.5%, 97%, and 98.3% intrusion detection accuracies, and 94.7%, 93.9%, and 97.2% recall, respectively, whereas the best models in the classical-only framework achieved 86.7%, 95%, and 89.7% intrusion detection accuracies, and 70.7%, 89.8, and 80.6% recall, respectively.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3304331