  A modern vehicle contains many electronic control units (ECUs), which
communicate with each other through the in-vehicle network to ensure vehicle
safety and performance. Emerging Connected and Automated Vehicles (CAVs) will
have more ECUs and coupling between them due to the vast array of additional
sensors, advanced driving features and Vehicle-to-Everything (V2X)
connectivity. Due to the connectivity, CAVs will be more vulnerable to remote
attackers. In this study, we developed a software-defined in-vehicle Ethernet
networking system that provides security against false information attacks. We
then created an attack model and attack datasets for false information attacks
on brake-related ECUs. After analyzing the attack dataset, we found that the
features of the dataset are time-series that have sequential variation
patterns. Therefore, we subsequently developed a long short term memory (LSTM)
neural network based false information attack/anomaly detection model for the
real-time detection of anomalies within the in-vehicle network. This attack
detection model can detect false information with an accuracy, precision and
recall of 95%, 95% and 87%, respectively, while satisfying the real-time
communication and computational requirements.
