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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Details
Title
Applied Machine Learning to Vehicle Security
Publication Details
Machine Intelligence and Big Data Analytics for Cybersecurity Applications, pp.423-442
Resource Type
Book chapter
Publisher
Springer International Publishing; Cham
Series
Studies in Computational Intelligence
Copyright
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Identifiers
99380465296106600
Academic Unit
Center for Cybersecurity; Center for Teaching, Learning, and Technology; College of Arts, Social Sciences, and Humanities