Abstract
The innovations in the interconnectivity of vehicles enable both expediency and insecurity. Surely, the convenience of gathering real-time information on traffic and weather conditions, the vehicle maintenance status, and the prevailing condition of the transport system at a macro level for infrastructure planning purposes is a boon to society. However, this newly found conveniences present unintended consequences. Specifically, the advancements on automation and connectivity are outpacing the developments in security and safety. We simply cannot afford to make the same mistakes similar to those that are prevalent in our critical infrastructures. Starting at the lowest level, numerous vulnerabilities have been identified in the internal communication network of vehicles. This study is a contribution towards the broad effort of securing the communication network of vehicles through the use of Machine Learning.
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Acknowledgements
This work is partially supported by the Florida Center for Cybersecurity, under grant # 3901-1009-00-A (2019 Collaborative SEED Program) and the National Security Agency under Grant Number H98230-19-1-0333. The United States Government is authorized to reproduce and distribute reprints notwithstanding any copyright notation herein.
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Francia, G.A., El-Sheikh, E. (2021). Applied Machine Learning to Vehicle Security. In: Maleh, Y., Shojafar, M., Alazab, M., Baddi, Y. (eds) Machine Intelligence and Big Data Analytics for Cybersecurity Applications. Studies in Computational Intelligence, vol 919. Springer, Cham. https://doi.org/10.1007/978-3-030-57024-8_19
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