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Publications in 2020 of type Conference Proceedings (English)

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    2020

    • Philipp Meyer, Timo H├Ąckel, Franz Korf, and Thomas C. Schmidt. Network Anomaly Detection in Cars based on Time-Sensitive Ingress Control. In: 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). Piscataway, NJ, USA, Nov. 2020, IEEE Press,
      [Abstract], [Fulltext Document (pdf)], [Slides (pdf)], [Bibtex]

      Connected cars need robust protection against network attacks. Network anomaly detection and prevention on board will be particularly fast and reliable when situated on the lowest possible layer. Blocking traffic on a low layer, however, causes severe harm if triggered erroneously by falsely positive alarms. In this paper, we introduce and evaluate a concept for detecting anomalous traffic using the ingress control of Time-Sensitive Networking (TSN). We build on the idea that already defined TSN traffic descriptors for in-car network configurations are rigorous, and hence any observed violation should not be a false positive. Also, we use Software-Defined Networking (SDN) technologies to collect and evaluate ingress anomaly reports, to identify the generating flows, and to ban them from the network. We evaluate our concept by simulating a real-world zonal network topology of a future car. Our findings confirm that abnormally behaving individual flows can indeed be reliably segregated with zero false positives.

      @InProceedings{   mhks-nadci-20,
        author        = {Philipp Meyer and Timo H{\"a}ckel and Franz Korf and
                        Thomas C. Schmidt},
        title         = {{Network Anomaly Detection in Cars based on Time-Sensitive
                        Ingress Control}},
        booktitle     = {2020 IEEE 92nd Vehicular Technology Conference
                        (VTC2020-Fall)},
        location      = {Online},
        month         = nov,
        year          = 2020,
        publisher     = {IEEE Press},
        address       = {Piscataway, NJ, USA},
        abstract      = {Connected cars need robust protection against network
                        attacks. Network anomaly detection and prevention on board
                        will be particularly fast and reliable when situated on the
                        lowest possible layer. Blocking traffic on a low layer,
                        however, causes severe harm if triggered erroneously by
                        falsely positive alarms. In this paper, we introduce and
                        evaluate a concept for detecting anomalous traffic using
                        the ingress control of Time-Sensitive Networking (TSN). We
                        build on the idea that already defined TSN traffic
                        descriptors for in-car network configurations are rigorous,
                        and hence any observed violation should not be a false
                        positive. Also, we use Software-Defined Networking (SDN)
                        technologies to collect and evaluate ingress anomaly
                        reports, to identify the generating flows, and to ban them
                        from the network. We evaluate our concept by simulating a
                        real-world zonal network topology of a future car. Our
                        findings confirm that abnormally behaving individual flows
                        can indeed be reliably segregated with zero false
                        positives.},
        groups        = {own, publications, simulation, tsn, security, sdn,
                        anomaly-detection},
        langid        = {english}
      }