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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.
Cited literature [30 references]
https://hal-cea.archives-ouvertes.fr/cea-01888831
Contributor : Léna Le Roy <>
Submitted on : Friday, October 5, 2018 - 3:00:44 PM
Last modification on : Friday, June 25, 2021 - 9:48:07 AM
Long-term archiving on: : Sunday, January 6, 2019 - 4:24:31 PM
Contributor : Léna Le Roy <>
Submitted on : Friday, October 5, 2018 - 3:00:44 PM
Last modification on : Friday, June 25, 2021 - 9:48:07 AM
Long-term archiving on: : Sunday, January 6, 2019 - 4:24:31 PM
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Russo2018.pdf
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