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Publications in 2021 of type Article (English)

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    2021

    • Philipp Meyer, Timo H├Ąckel, Sandra Reider, Franz Korf, and Thomas C. Schmidt. Network Anomaly Detection in Cars: A Case for Time-Sensitive Stream Filtering and Policing. In: . Dec. 2021,
      [Abstract], [ArXiv], [Bibtex]

      Connected cars are vulnerable to cyber attacks. Security challenges arise from vehicular management uplinks, from signaling with roadside units or nearby cars, as well as from common Internet services. Major threats arrive from bogus traffic that enters the in-car backbone, which will comprise of Ethernet technologies in the near future. Various security techniques from different areas and layers are under discussion to protect future vehicles. In this paper, we show how Per-Stream Filtering and Policing of IEEE Time-Sensitive Networking (TSN) can be used as a core technology for identifying misbehaving traffic flows in cars, and thereby serve as network anomaly detectors. TSN is the leading candidate for implementing quality of service in vehicular Ethernet backbones. We classify the impact of network attacks on traffic flows and benchmark the detection performance in each individual class. Based on a backbone topology derived from a real car and its traffic definition, we evaluate the detection system in realistic scenarios with real attack traces. Our results show that the detection accuracy depends on the precision of the in-vehicle communication specification, the traffic type, the corruption layer, and the attack impact on the link layer. Most notably, the anomaly indicators of our approach remain free of false positive alarms, which is an important foundation for implementing automated countermeasures in future vehicles.

      @Article{         mhrks-nadct-21,
        author        = {Philipp Meyer AND Timo H{\"a}ckel AND Sandra Reider AND
                        Franz Korf AND Thomas C. Schmidt},
        title         = {{Network Anomaly Detection in Cars: A Case for
                        Time-Sensitive Stream Filtering and Policing}},
        month         = dec,
        year          = 2021,
        eprinttype    = {arxiv},
        eprint        = {2112.11109},
        abstract      = {Connected cars are vulnerable to cyber attacks. Security
                        challenges arise from vehicular management uplinks, from
                        signaling with roadside units or nearby cars, as well as
                        from common Internet services. Major threats arrive from
                        bogus traffic that enters the in-car backbone, which will
                        comprise of Ethernet technologies in the near future.
                        Various security techniques from different areas and layers
                        are under discussion to protect future vehicles. In this
                        paper, we show how Per-Stream Filtering and Policing of
                        IEEE Time-Sensitive Networking (TSN) can be used as a core
                        technology for identifying misbehaving traffic flows in
                        cars, and thereby serve as network anomaly detectors. TSN
                        is the leading candidate for implementing quality of
                        service in vehicular Ethernet backbones. We classify the
                        impact of network attacks on traffic flows and benchmark
                        the detection performance in each individual class. Based
                        on a backbone topology derived from a real car and its
                        traffic definition, we evaluate the detection system in
                        realistic scenarios with real attack traces. Our results
                        show that the detection accuracy depends on the precision
                        of the in-vehicle communication specification, the traffic
                        type, the corruption layer, and the attack impact on the
                        link layer. Most notably, the anomaly indicators of our
                        approach remain free of false positive alarms, which is an
                        important foundation for implementing automated
                        countermeasures in future vehicles.},
        groups        = {own, publications, simulation},
        langid        = {english}
      }