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Anomaly Detection in Vehicle-to-Infrastructure Communications

Abstract : 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.
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Submitted on : Friday, October 5, 2018 - 3:00:44 PM
Last modification on : Thursday, February 17, 2022 - 10:08:05 AM
Long-term archiving on: : Sunday, January 6, 2019 - 4:24:31 PM


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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⟩



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