The rapid development of intelligent and connected vehicle (ICV) has brought increasing communication data and valuable data assets. However, the diversity and complexity of ICV data make it hard to carry out data security risk assessment. Aiming at the problem, this paper proposes an effective analytical method for the data security risk assessment method of ICVs. Considering the typical scenario for the generated data of ICVs, a data security classification and grading framework is constructed from the aspects of in-vehicle data, personal data, and external environment data. The data security risk assessment model is built for ICVs and includes functional modules such as asset identification, threat identification, impact assessment, vulnerability identification, and risk assessment. This work provides methods and security management and control strategy support for ICV data security risk assessment.


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    Titel :

    Data Security Risk Assessment Method of Intelligent and Connected Vehicles Based on Data Security Classification and Grading


    Beteiligte:
    Wang, Liyong (Autor:in) / Jin, Long (Autor:in) / Ji, Haojie (Autor:in) / Wang, Jingyan (Autor:in) / Chen, Yupeng (Autor:in) / Fang, Junzhe (Autor:in)


    Erscheinungsdatum :

    28.10.2023


    Format / Umfang :

    769073 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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