%0 Conference Paper %F Oral %T Anomaly Detection in Vehicle-to-Infrastructure Communications %+ Département Intelligence Ambiante et Systèmes Interactifs (DIASI) %A Russo, Michele %A Labonne, Maxime %A Olivereau, Alexis %A Rmayti, Mohammad %Z This work has received funding from the European Union Horizon 2020 research and innovation program as part of theVI-DAS project, under the grant agreement No 690772. The authors wish to thank the project partner Intempora for providing the RTMaps software and their technical support. We thank the TU/e Eindhoven for providing the driving data upon which this research has been carried out %< avec comité de lecture %B 2018 IEEE 87th Vehicular Technology Conference (VTC Spring) %C Porto, Portugal %I IEEE %8 2018-06-03 %D 2018 %R 10.1109/VTCSpring.2018.8417863 %K Anomaly detection %K LSTM %K MLP %K V2I %K forecasting %K benchmarking %Z Computer Science [cs] %Z Engineering Sciences [physics]Conference papers %X 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. %G English %2 https://cea.hal.science/cea-01888831/document %2 https://cea.hal.science/cea-01888831/file/Russo2018.pdf %L cea-01888831 %U https://cea.hal.science/cea-01888831 %~ CEA %~ DRT %~ CEA-UPSAY %~ UNIV-PARIS-SACLAY %~ CEA-UPSAY-SACLAY %~ LIST %~ GS-ENGINEERING %~ GS-COMPUTER-SCIENCE