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Détection autonome de trafic malveillant dans les réseaux véhiculaires

Quentin Ricard 1
1 LAAS-SARA - Équipe Services et Architectures pour Réseaux Avancés
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : The growth of intelligent transport systems brings new highly connected vehicles on the roads of the world. These vehicles now embed new devices and services meant to increase road safety, reduce the environmental impact of the vehicles and improve the user experience. However, these new communication channels between vehicles and the rest of the world, especially cellular networks bring new vulnerabilities. Vehicles are now depending on the information provided by the network and are therefore subject to malfunction and anomalies due to such network. Worse, they become vulnerable to malicious actors of the cyber-space. Mainstream information networks have been confronted with security problems for a long time. Numerous approaches have been designed in order to detect anomalies an intrusion inside such networks. However, these methods cannot be applied directly to the automotive context. In fact, the specific nature of the communications, the anomalies and the execution of intrusion detection systems inside the vehicles must be considered. Therefore, we present a new anomaly detection system dedicated to vehicular networks and their vulnerabilities. Our detection is based on the creation of instantaneous description windows that are linked together thanks to an ontology. Thanks to these relations, the results of the detection are fed with the communication context of the vehicle during an anomaly. Consequently, the diagnostic from the administrator is made easier and we ensure the traceability of the anomaly. We evaluate the performances of our system thanks to a dataset produced by our tool named Autobot. It produces realistic communications, anomalies and attacks on cellular vehicular networks. We aim to evaluate our system based on the quality of the detection of different kinds of attacks while minimizing the number of false positives. We compare the results of two unsupervised machine learning algorithms that are used during the detection named HTM and LSTM.
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Submitted on : Friday, March 5, 2021 - 2:25:10 PM
Last modification on : Thursday, June 10, 2021 - 3:04:00 AM


Version validated by the jury (STAR)


  • HAL Id : tel-02966530, version 2


Quentin Ricard. Détection autonome de trafic malveillant dans les réseaux véhiculaires. Systèmes embarqués. Université Paul Sabatier - Toulouse III, 2020. Français. ⟨NNT : 2020TOU30149⟩. ⟨tel-02966530v2⟩



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