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Towards Synthetic Data Generation of VANET Attacks for Efficient Testing
RISE Research Institutes of Sweden, Safety and Transport, Maritime department.ORCID iD: 0000-0002-9587-3423
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.ORCID iD: 0000-0003-1908-3136
2023 (English)In: 2023 IEEE Intelligent Vehicles Symposium (IV), 2023Conference paper, Published paper (Refereed)
Abstract [en]

Vehicle-to-Vehicle communication can improve traffic safety and efficiency. This technology, however, increases the attack surface, making new attacks possible. To cope with these threats, researchers have made a great effort to identify and explore the potential of cyberattacks and also proposed various intrusion or misbehaviour detection systems, in particular machine learning-based solutions. Simulations have become essential to design and evaluate such detection systems as there are no real publicly available Vehicular Ad-Hoc Network (VANET) datasets containing a variety of attacks. The drawback is that simulations require a significant amount of computational resources and time for configuration. In this paper, we present an attack simulation and generation framework that allows training the attack generator with either simulated or real VANET attacks. We outline the structure of our proposed framework and describe the setup of a standard-compliant attack simulator that generates valid standardised CAM and DENM messages specified by ETSI in the Cooperative Intelligent Transport Systems (C-ITS) standards. Based on the introduced framework, we demonstrate the feasibility of using deep learning for the generation of VANET attacks, which ultimately allows us to test and verify prototypes without running resource-demanding simulations.

Place, publisher, year, edition, pages
2023.
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:ri:diva-66341DOI: 10.1109/iv55152.2023.10186685OAI: oai:DiVA.org:ri-66341DiVA, id: diva2:1794481
Conference
2023 IEEE Intelligent Vehicles Symposium (IV). 4-7 June 2023
Available from: 2023-09-05 Created: 2023-09-05 Last updated: 2024-02-28Bibliographically approved

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Rosenstatter, ThomasMelnyk, Kateryna

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