The cybersecurity of connected cars, integral to the broader Internet of Things (IoT) landscape, has become of paramount concern. Cyber-attacks, including hijacking and spoofing, pose significant threats to these technological advance-ments, potentially leading to unauthorized control over vehicular networks or creating deceptive identities. Given the difficulty of deploying comprehensive defensive logic across all vehicles, this paper presents a novel approach for identifying potential attacks through Radio Access Network (RAN) event monitoring. The major contribution of this paper is a location anomaly detection module that identifies aberrant devices that appear in multiple locations simultaneously - a potential indicator of a hijacking attack. We demonstrate how RAN-event based location anomaly detection is effective in combating malicious activity targeting connected cars. Using RAN data generated by tens of millions of connected cars, we developed a fast and efficient method for identifying potential malicious or rogue devices. The implications of this research are far-reaching. By increasing the security of connected cars, we can enhance the safety of users, provide robust defenses for the automotive industry, and improve overall cybersecurity practices for IoT devices.


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    Titel :

    Navigating Connected Car Cybersecurity: Location Anomaly Detection with RAN Data


    Beteiligte:
    Wang, Feng (Autor:in) / Koral, Yaron (Autor:in) / Futamura, Kenichi (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    506260 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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