A language-based intrusion detection approach for automotive embedded networks

Abstract : The increase in connectivity and complexity of modern automotive networks presents new opportunities for potential hackers trying to take over a vehicle. To protect the automotive networks from such attacks, security mechanisms, such as firewalls or secure authentication protocols may be included. However, should an attacker succeed in bypassing such measures and gain access to the internal network, these security mechanisms become unable to report about the attacks ensuing such a breach, occurring from the internal network. To complement these preventive security mechanisms, we present a non intrusive network-based intrusion detection approach fit for vehicular networks, such as the widely used CAN. Leveraging the high predictability of embedded automotive systems, we use language theory to elaborate a set of attack signatures derived from behavioural models of the automotive calculators in order to detect a malicious sequence of messages transiting through the internal network.
Type de document :
Communication dans un congrès
IEEE Pacific Rim International Symposium on Dependable Computing ( PRDC ), Nov 2015, Zhangjiajie, China
Liste complète des métadonnées

https://hal.laas.fr/hal-01967640
Contributeur : Mohamed Kaaniche <>
Soumis le : mardi 1 janvier 2019 - 09:54:09
Dernière modification le : vendredi 11 janvier 2019 - 01:10:44

Fichier

prdcsoumis.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01967640, version 1

Citation

Ivan Studnia, Eric Alata, Vincent Nicomette, Mohamed Kaâniche, Youssef Laarouchi. A language-based intrusion detection approach for automotive embedded networks. IEEE Pacific Rim International Symposium on Dependable Computing ( PRDC ), Nov 2015, Zhangjiajie, China. 〈hal-01967640〉

Partager

Métriques

Consultations de la notice

5

Téléchargements de fichiers

3