HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download

https://hal-cea.archives-ouvertes.fr/cea-01888831
Contributor : Léna Le Roy Connect in order to contact the contributor
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

File

Russo2018.pdf
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

121

Files downloads

281