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