RulEth: Genetic Programming-Driven Derivation of Security Rules for Automotive Ethernet

Author:

Gail Felix ClemensORCID,Rieke RolandORCID,Fenzl FlorianORCID

Publisher

Springer Nature Switzerland

Reference37 articles.

1. Upstream: 2023 global automotive cybersecurity report (2023). https://upstream.auto/reports/global-automotive-cybersecurity-report/. Accessed 19 June 2023

2. Sommer, R., Paxson, V.: Outside the closed world: on using machine learning for network intrusion detection. In: IEEE Symposium on Security and Privacy 2010, pp. 305–316 (2010)

3. Lundberg, H.: Increasing the trustworthiness of AI-based in-vehicle ids using explainable AI. Master’s thesis, Mid Sweden University (2022)

4. Rastogi, N., Rampazzi, S., Clifford, M., Heller, M., Bishop, M., Levitt, K.: Explaining radar features for detecting spoofing attacks in connected autonomous vehicles (2022). https://arxiv.org/abs/2203.00150

5. European Commission: Directorate-General for Communications Networks, Content and Technology, Ethics guidelines for trustworthy AI. Publications Office (2019)

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