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A modern vehicle contains many electronic control units (ECUs), whichcommunicate with each other through the in-vehicle network to ensure vehiclesafety and performance. Emerging Connected and Automated Vehicles (CAVs) willhave more ECUs and coupling between them due to the vast array of additionalsensors, advanced driving features and Vehicle-to-Everything (V2X)connectivity. Due to the connectivity, CAVs will be more vulnerable to remoteattackers. In this study, we developed a software-defined in-vehicle Ethernetnetworking system that provides security against false information attacks. Wethen created an attack model and attack datasets for false information attackson brake-related ECUs. After analyzing the attack dataset, we found that thefeatures of the dataset are time-series that have sequential variationpatterns. Therefore, we subsequently developed a long short term memory (LSTM)neural network based false information attack/anomaly detection model for thereal-time detection of anomalies within the in-vehicle network. This attackdetection model can detect false information with an accuracy, precision andrecall of 95%, 95% and 87%, respectively, while satisfying the real-timecommunication and computational requirements.

Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network