Anomaly Detection in Vehicle-to-Infrastructure Communications - Archive ouverte HAL Access content directly
Conference Papers Year :

Anomaly Detection in Vehicle-to-Infrastructure Communications

(1) , (1) , (1) , (1)
1

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.
Fichier principal
Vignette du fichier
Russo2018.pdf (827.56 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

cea-01888831 , version 1 (05-10-2018)

Identifiers

Cite

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⟩
136 View
328 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More