  With the rise of self-drive cars and connected vehicles, cars are equipped
with various devices to assistant the drivers or support self-drive systems.
Undoubtedly, cars have become more intelligent as we can deploy more and more
devices and software on the cars. Accordingly, the security of assistant and
self-drive systems in the cars becomes a life-threatening issue as smart cars
can be invaded by malicious attacks that cause traffic accidents. Currently,
canonical machine learning and deep learning methods are extensively employed
in car hacking detection. However, machine learning and deep learning methods
can easily be overconfident and defeated by carefully designed adversarial
examples. Moreover, those methods cannot provide explanations for security
engineers for further analysis. In this work, we investigated Deep Bayesian
Learning models to detect and analyze car hacking behaviors. The Bayesian
learning methods can capture the uncertainty of the data and avoid
overconfident issues. Moreover, the Bayesian models can provide more
information to support the prediction results that can help security engineers
further identify the attacks. We have compared our model with deep learning
models and the results show the advantages of our proposed model. The code of
this work is publicly available
