Automated Interpretation and Reduction of In-Vehicle Network Traces at a Large Scale

Abstract

In modern vehicles, high communication complexity requires cost-effective integration tests such as data-driven system verification with in-vehicle network traces. With the growing amount of traces, distributable Big Data solutions for analyses become essential to inspect massive amounts of traces. Such traces need to be processed systematically using automated procedures, as manual steps become infeasible due to loading and processing times in existing tools. Further, trace analyses require multiple domains to verify the system in terms of different aspects (e.g., specific functions) and thus, require solutions that can be parameterized towards respective domains. Existing solutions are not able to process such trace amounts in a flexible and automated manner. To overcome this, we introduce a fully automated and parallelizable end-to-end preprocessing framework that allows to analyze massive in-vehicle network traces. Being parameterized per domain, trace data is systematically reduced and extended with domain knowledge, yielding a representation targeted towards domain-specific system analyses. We show that our approach outperforms existing solutions in terms of execution time and extensibility by evaluating our approach on three real-world data sets from the automotive industry.