Anomaly Detection in Vehicle-to-Infrastructure Communications

Michele Russo; Maxime Labonne; Alexis Olivereau; Mohammad Rmayti

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

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.

Keywords : MLP V2I forecasting benchmarking LSTM Anomaly detection

Document type :

Conference papers

Domain :

Computer Science [cs]

Engineering Sciences [physics]

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https://hal-cea.archives-ouvertes.fr/cea-01888831
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Submitted on : Friday, October 5, 2018 - 3:00:44 PM
Last modification on : Thursday, February 7, 2019 - 4:54:10 PM
Long-term archiving on : Sunday, January 6, 2019 - 4:24:31 PM

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