Attak Detection in RSU-OBU Communication Using Deep Neural Network

Abstract

This paper proposes three types of neural network-based attack detectors for Internet of Vehicles (IoV) networks. The proposed attack detectors consist of Long Short-Term Memory (LSTM) layers. The fault detector is implemented in the Road Side Unit (RSU) that communicates with On Board Unit (OBU) installed in the autonomous vehicles. We consider the multiplicative attacks in the RSU-OBU communication messages. The training and validation data sets are generated by using an automated driving toolbox in MATLAB. Attack detector performance is evaluated using the validation data set that is not included in the training data sets. Performance comparison of the three detectors are given. © 2023 ICROS.