  As the central nerve of the intelligent vehicle control system, the
in-vehicle network bus is crucial to the security of vehicle driving. One of
the best standards for the in-vehicle network is the Controller Area Network
(CAN bus) protocol. However, the CAN bus is designed to be vulnerable to
various attacks due to its lack of security mechanisms. To enhance the security
of in-vehicle networks and promote the research in this area, based upon a
large scale of CAN network traffic data with the extracted valuable features,
this study comprehensively compared fully-supervised machine learning with
semi-supervised machine learning methods for CAN message anomaly detection.
Both traditional machine learning models (including single classifier and
ensemble models) and neural network based deep learning models are evaluated.
Furthermore, this study proposed a deep autoencoder based semi-supervised
learning method applied for CAN message anomaly detection and verified its
superiority over other semi-supervised methods. Extensive experiments show that
the fully-supervised methods generally outperform semi-supervised ones as they
are using more information as inputs. Typically the developed XGBoost based
model obtained state-of-the-art performance with the best accuracy (98.65%),
precision (0.9853), and ROC AUC (0.9585) beating other methods reported in the
literature.
