Anomaly Detection in Vehicle-to-Infrastructure Communications

Michele Russo (1) , Maxime Labonne (1) , Alexis Olivereau (1) , Mohammad Rmayti (1)

1 DIASI - Département Intelligence Ambiante et Systèmes Interactifs

This paper presents a neural network-based anomaly detection system for vehicular communications. The proposed system is able to detect in-vehicle data tampering in order to avoid the transmission of bogus or harmful information. We investigate the use of Long Short-term Memory (LSTM) and Multilayer Perceptron (MLP) neural networks to build two prediction models. For each model, an efficient architecture is designed based on appropriate hardware requirements. Then, a comparative performance analysis is provided to recommend the most efficient neural network model. Finally, a set of metrics are selected to show the accuracy of the proposed detection system under several types of security attacks.

Keywords: Anomaly detection; LSTM; MLP; V2I; forecasting; benchmarking

Domains: Computer Science [cs]; Engineering Sciences [physics]

Michele Russo, Maxime Labonne, Alexis Olivereau, Mohammad Rmayti. Anomaly Detection in Vehicle-to-Infrastructure Communications. 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Jun 2018, Porto, Portugal. ⟨10.1109/VTCSpring.2018.8417863⟩. ⟨cea-01888831⟩