Anomaly detection techniques have been applied to the challenging problem of ensuring both cybersecurity and safety of connected vehicles. We propose a taxonomy of prior research in this domain. Our proposed taxonomy has 3 overarching dimensions subsuming 9 categories and 38 subcategories. Key observations emerging from the survey are: Real-world datasets are seldom used, but instead, most results are derived from simulations; V2V/V2I communications and in vehicle communication are not considered together; proposed techniques are seldom evaluated against a baseline; safety of the vehicles does not attract as much attention as cybersecurity.


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    Title :

    A Survey of Anomaly Detection for Connected Vehicle Cybersecurity and Safety




    Publication date :

    2018-06-01


    Size :

    820674 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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