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As in-vehicle communication becomes more complex, the automotive community is exploring various architectural options such as centralized and zonal architectures for their numerous benefits. Common characteristics of these architectures include the need for high-bandwidth communication and security, which have been elusive with standard automotive architectures. Further, as automotive communication technologies evolve, it is also likely that multiple link-layer technologies such as CAN and Automotive Ethernet will co-exist. These alternative architectures promise to integrate these diverse sets of technologies. However, architectures that allow such co-existence have not been adequately explored. In this work we explore a new network architecture called Named Data Networking (NDN) to achieve multiple goals: provide a foundational security infrastructure and bridge different link layer protocols such as CAN, LIN, and automotive Ethernet into a unified communication system. We have created a proof-of-concept bench-top testbed using CAN HATS and Raspberry PIs that replay real traffic over CAN and Ethernet to demonstrate how NDN can provide a secure, high-speed bridge between different automotive link layers. We also show how NDN can support communication between centralized or zonal high-power compute components. Security is achieved through digitally signing all Data packets between these components, preventing unauthorized ECUs from injecting arbitrary data into the network. We also demonstrate NDN's ability to prevent DoS and replay attacks between different network segments connected through NDN.
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Capture and Analysis of Traffic Traces on a Wide-Area NDN Testbed
High-quality traffic measurements from realistic deployments are essential for understanding and improving new network technologies. For Named Data Networking, collecting such measurements is difficult because real-world deployments are limited. To address this problem, we created a dataset of NDN traffic traces and a toolkit for capturing, analyzing, and replaying them. The dataset is collected from real routers on the official NDN testbed and is the first large, non-synthetic NDN dataset available to the research community. This paper presents the dataset and tools, describes their properties, and shares insights useful for broader NDN research.
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- PAR ID:
- 10647727
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 101 to 108
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3623565.3623707
