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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Navigating Connected Car Cybersecurity: Location Anomaly Detection with RAN Data


    Contributors:


    Publication date :

    2024-06-24


    Size :

    506260 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A SURVEY OF ANOMALY DETECTION FOR CONNECTED VEHICLE CYBERSECURITY AND SAFETY

    Rajbahadur, Gopi Krishnan / Malton, Andrew J. / Walenstein, Andrew et al. | British Library Conference Proceedings | 2018


    A Survey of Anomaly Detection for Connected Vehicle Cybersecurity and Safety

    Rajbahadur, Gopi Krishnan / Malton, Andrew J. / Walenstein, Andrew et al. | IEEE | 2018


    Detection method for Cybersecurity attack on Connected vehicles

    Dadam, Sumanth Reddy / Zhu, Di / Kumar, Vivek et al. | British Library Conference Proceedings | 2021



    Advanced Analytics for Connected Car Cybersecurity

    Levi, Matan / Allouche, Yair / Kontorovich, Aryeh | IEEE | 2018