Publications in 2020 of type Article, Conference Proceedings and Edited Conference Proceedings
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2020
- Timo Häckel, Anja Schmidt, Philipp Meyer, Franz Korf, and Thomas C. Schmidt. Strategies for Integrating Controls Flows in Software-Defined In-Vehicle Networks and Their Impact on Network Security. In: 2020 IEEE Vehicular Networking Conference (VNC) (IEEE VNC 2020). Piscataway, NJ, USA, Dec. 2020, IEEE Press,
[Abstract], [DOI], [Bibtex]Current In-Vehicle Networks (IVNs) connect Electronic Control Units (ECUs) via domain busses. A gateway forwards messages between these domains. Automotive Ethernet emerges as a flat, high-speed backbone technology for IVNs that carries the various control flows within Ethernet frames. Recently, Software-Defined Networking (SDN) has been identified as a useful building block of the vehicular domain, as it allows the differentiation of packets based on all header fields and thus can isolate unrelated control flows. In this work, we systematically explore the different strategies for integrating automotive control flows in switched Ether-networks and analyze their security impact for a software-defined IVN. We discuss how control flow identifiers can be embedded on different layers resulting in a range of solutions from fully exposed embedding to deep encapsulation. We evaluate these strategies in a realistic IVN based on the communication matrix of a production grade vehicle, which we map into a modern Ethernet topology. We find that visibility of automotive control flows within packet headers is essential for the network infrastructure to enable isolation and access control. With an exposed embedding, the SDN backbone can establish and survey trust zones within the IVN and largely reduce the attack surface of connected cars. An exposed embedding strategy also minimizes communication expenses.
@InProceedings{ hsmks-sicfs-20, author = {Timo H{\"a}ckel and Anja Schmidt and Philipp Meyer and Franz Korf and Thomas C. Schmidt}, title = {{Strategies for Integrating Controls Flows in Software-Defined In-Vehicle Networks and Their Impact on Network Security}}, booktitle = {2020 IEEE Vehicular Networking Conference (VNC) (IEEE VNC 2020)}, location = {Online}, month = dec, year = 2020, publisher = {IEEE Press}, address = {Piscataway, NJ, USA}, doi = {10.1109/VNC51378.2020.9318372}, abstract = {Current In-Vehicle Networks (IVNs) connect Electronic Control Units (ECUs) via domain busses. A gateway forwards messages between these domains. Automotive Ethernet emerges as a flat, high-speed backbone technology for IVNs that carries the various control flows within Ethernet frames. Recently, Software-Defined Networking (SDN) has been identified as a useful building block of the vehicular domain, as it allows the differentiation of packets based on all header fields and thus can isolate unrelated control flows. In this work, we systematically explore the different strategies for integrating automotive control flows in switched Ether-networks and analyze their security impact for a software-defined IVN. We discuss how control flow identifiers can be embedded on different layers resulting in a range of solutions from fully exposed embedding to deep encapsulation. We evaluate these strategies in a realistic IVN based on the communication matrix of a production grade vehicle, which we map into a modern Ethernet topology. We find that visibility of automotive control flows within packet headers is essential for the network infrastructure to enable isolation and access control. With an exposed embedding, the SDN backbone can establish and survey trust zones within the IVN and largely reduce the attack surface of connected cars. An exposed embedding strategy also minimizes communication expenses.}, groups = {own, sdn, publications, security} } - Philipp Meyer, Timo Häckel, Falk Langer, Lukas Stahlbock, Jochen Decker, Sebastian A. Eckhardt, Franz Korf, Thomas C. Schmidt, and Fabian Schüppel. Demo: A Security Infrastructure for Vehicular Information Using SDN, Intrusion Detection, and a Defense Center in the Cloud. In: 2020 IEEE Vehicular Networking Conference (VNC) (IEEE VNC 2020). Piscataway, NJ, USA, Dec. 2020, IEEE Press,
[Abstract], [DOI], [Bibtex]Vehicular on-board communication is the basis for advanced driver assistance, autonomous driving, over-the-air updates, and many more. If unprotected, this infrastructure is vulnerable to manipulation and various attacks. As any networked system, future connected cars require robust protection, monitoring, and incidence management against cyber-attacks during their lifetime. We demonstrate an infrastructure that secures the in-vehicle communication system and enables the security management of an entire vehicle fleet. Our prototype - a real-world production car - uses an Ethernet backbone network. It implements protective measures using software-defined networking, anomaly detection technologies, and is connected to a cyber defense center in the cloud. We demonstrate how this combination can reliably detect and mitigate common attacks on the vehicle - including its legacy components.
@InProceedings{ mhlsd-dsivi-20, author = {Philipp Meyer and Timo H{\"a}ckel and Falk Langer and Lukas Stahlbock and Jochen Decker and Sebastian A. Eckhardt and Franz Korf and Thomas C. Schmidt and Fabian Sch{\"u}ppel}, title = {{Demo: A Security Infrastructure for Vehicular Information Using {SDN,} Intrusion Detection, and a Defense Center in the Cloud}}, booktitle = {2020 IEEE Vehicular Networking Conference (VNC) (IEEE VNC 2020)}, location = {Online}, month = dec, year = 2020, publisher = {IEEE Press}, address = {Piscataway, NJ, USA}, doi = {10.1109/VNC51378.2020.9318351}, abstract = {Vehicular on-board communication is the basis for advanced driver assistance, autonomous driving, over-the-air updates, and many more. If unprotected, this infrastructure is vulnerable to manipulation and various attacks. As any networked system, future connected cars require robust protection, monitoring, and incidence management against cyber-attacks during their lifetime. We demonstrate an infrastructure that secures the in-vehicle communication system and enables the security management of an entire vehicle fleet. Our prototype - a real-world production car - uses an Ethernet backbone network. It implements protective measures using software-defined networking, anomaly detection technologies, and is connected to a cyber defense center in the cloud. We demonstrate how this combination can reliably detect and mitigate common attacks on the vehicle - including its legacy components.}, groups = {own, sdn, publications, security, anomaly-detection} } - Randolf Rotermund, Timo Häckel, Philipp Meyer, Franz Korf, and Thomas C. Schmidt. Requirements Analysis and Performance Evaluation of SDN Controllers for Automotive Use Cases. In: 2020 IEEE Vehicular Networking Conference (VNC) (IEEE VNC 2020). Piscataway, NJ, USA, Dec. 2020, IEEE Press,
[Abstract], [Slides (pdf)], [DOI], [Bibtex]Future vehicles will be more connected than ever leading to increased dynamics in vehicle on-board networks. Software-Defined Networking (SDN) is a promising technology to meet the emerging needs for flexibility and security in future automotive use cases. Although SDN controllers have been evaluated in data center networks, to the best of our knowledge there is a lack of an analysis and performance evaluation of SDN controllers for automotive use cases. In this work we provide a detailed requirements analysis for the use of SDN controllers in cars. Based on this requirements analysis we choose existing controller implementations for a performance analysis. Finally, we analyze automotive specific use cases for SDN controllers with controller application examples and show how these can fulfill additional requirements. Our evaluation provides a helpful basis for the design and development of SDN controllers that can be used in vehicles.
@InProceedings{ rhmks-rapesc-20, author = {Randolf Rotermund and Timo H{\"a}ckel and Philipp Meyer and Franz Korf and Thomas C. Schmidt}, title = {{Requirements Analysis and Performance Evaluation of {SDN} Controllers for Automotive Use Cases}}, booktitle = {2020 IEEE Vehicular Networking Conference (VNC) (IEEE VNC 2020)}, location = {Online}, month = dec, year = 2020, publisher = {IEEE Press}, address = {Piscataway, NJ, USA}, doi = {10.1109/VNC51378.2020.9318378}, abstract = {Future vehicles will be more connected than ever leading to increased dynamics in vehicle on-board networks. Software-Defined Networking (SDN) is a promising technology to meet the emerging needs for flexibility and security in future automotive use cases. Although SDN controllers have been evaluated in data center networks, to the best of our knowledge there is a lack of an analysis and performance evaluation of SDN controllers for automotive use cases. In this work we provide a detailed requirements analysis for the use of SDN controllers in cars. Based on this requirements analysis we choose existing controller implementations for a performance analysis. Finally, we analyze automotive specific use cases for SDN controllers with controller application examples and show how these can fulfill additional requirements. Our evaluation provides a helpful basis for the design and development of SDN controllers that can be used in vehicles.}, groups = {own, sdn, automotive, performance-analysis, publications} } - Philipp Meyer, Timo Häckel, 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). Pages 1—5, Piscataway, NJ, USA, Nov. 2020, IEEE Press,
[Abstract], [Fulltext Document (pdf)], [Slides (pdf)], [DOI], [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, pages = {1--5}, publisher = {IEEE Press}, address = {Piscataway, NJ, USA}, doi = {10.1109/VTC2020-Fall49728.2020.9348746}, 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} }