  The Controller Area Network (CAN) bus works as an important protocol in the
real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust
architecture. The risk of IVN devices has still been insecure and vulnerable
due to the complex data-intensive architectures which greatly increase the
accessibility to unauthorized networks and the possibility of various types of
cyberattacks. Therefore, the detection of cyberattacks in IVN devices has
become a growing interest. With the rapid development of IVNs and evolving
threat types, the traditional machine learning-based IDS has to update to cope
with the security requirements of the current environment. Nowadays, the
progression of deep learning, deep transfer learning, and its impactful outcome
in several areas has guided as an effective solution for network intrusion
detection. This manuscript proposes a deep transfer learning-based IDS model
for IVN along with improved performance in comparison to several other existing
models. The unique contributions include effective attribute selection which is
best suited to identify malicious CAN messages and accurately detect the normal
and abnormal activities, designing a deep transfer learning-based LeNet model,
and evaluating considering real-world data. To this end, an extensive
experimental performance evaluation has been conducted. The architecture along
with empirical analyses shows that the proposed IDS greatly improves the
detection accuracy over the mainstream machine learning, deep learning, and
benchmark deep transfer learning models and has demonstrated better performance
for real-time IVN security.
