loading page

A Novel Hybrid Quantum-Classical Framework for an In-vehicle Controller Area Network Intrusion Detection
  • +6
  • M Sabbir Salek ,
  • Pronab Kumar Biswas ,
  • Jacquan Pollard ,
  • Jordyn Hales ,
  • Zecheng Shen ,
  • Vivek Dixit ,
  • Mashrur Chowdhury ,
  • Sakib Mahmud Khan ,
  • Yao Wang
M Sabbir Salek
Clemson University, Clemson University

Corresponding Author:[email protected]

Author Profile
Pronab Kumar Biswas
Author Profile
Jacquan Pollard
Author Profile
Jordyn Hales
Author Profile
Zecheng Shen
Author Profile
Vivek Dixit
Author Profile
Mashrur Chowdhury
Author Profile
Sakib Mahmud Khan
Author Profile


In-vehicle controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based communication nature. An attacker can inject false messages to a vehicle’s CAN via wireless communication, the infotainment system, or the onboard diagnostic port. Thus, an effective intrusion detection system is essential to distinguish authentic CAN messages from false ones. 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 to feature extraction from CAN images generated from a vehicle’s CAN bus data. In contrast, the quantum RBM is dedicated to CAN image reconstruction for classification-based intrusion detection. The novelty of the study lies in utilizing the generative ability of the RBM to reconstruct the pixels in a CAN image, a portion of which is dedicated to labeling. Then, that portion of the reconstructed image is used to classify the image as an attack image or a normal image. 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. In contrast, the best models in the classical-only framework achieved 92.5%, 95%, and 93.3% intrusion detection accuracies and 84.2%, 89.8%, and 88.9% recall, respectively.
2023Published in IEEE Access volume 11 on pages 96081-96092. 10.1109/ACCESS.2023.3304331