How to Cite
Abstract
Our work specifically addresses In-Vehicle Network (IVN) architectures that address cyber anomalous changes as they take place in the vehicle network. The contributions in this paper are threefold. We propose solution methods to incorporate cyber defense for Byzantine-resilient, adaptive networks that weigh attributes such as efficiency or accuracy under a known, large number of novel or old attacks. These types of methods can handle large data-to-signal ratios rather effectively in sampled data environments. We also propose direct solutions for small networks whose time-frames in practice may or may not be useful for actual situations because of the overhead required to process the data required. Several experimental tests were conducted and results indicate that our proposed solutions vastly exceed baseline performance and could be useful for cyber defense research in various types of networks.
Autonomous vehicles (AVs) represent a major opportunity for the transportation industry and are a primary focus for both established and start-up companies. Over the past few years, many AVs have been developed and are running on the highways with minimal cyber defense provided by their developers. The trend is growing, with firms such as Waymo, Uber, and Zoox leading the pack in field deployment. With vehicle-to-everything (V2X) communication technologies, especially 5G, about to be deployed, there is now time to seriously consider the growing cyber security threats to large AV networks and focus on designing countermeasures.
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