Monitoring Automotive Software Security Health through Trustworthiness Score

Monitoring Automotive Software Security Health through Trustworthiness Score

연구 분야: Strategies

학회: CSCS '23: Proceedings of the 7th ACM Computer Science in Cars Symposium

The automotive industry is drastically moving towards autonomous. This trend constitutes in a fundamental change of going from mechanical and electrical engineering towards software-driven approaches. Modern vehicles can embed more than hundred electronic control units (ECUs). As autonomous vehicles require more intelligence as well as more computing power, high-performance computers (HPCs) bring the data management capabilities for cloud and IoT services to support the transition to a service-oriented vehicle system architecture. With this growing reliance on software in vehicles, software reliability and trustworthiness are increasingly critical to vehicle security. Measuring security trustworthiness in automotive software is even more valuable as cybersecurity is shifting to the left, i.e. in the early phase of development and design process. In this article, we propose a novel method for evaluating security trustworthiness of automotive software by leveraging a computational trust model. The method consists of selecting different domains contributing to software security, calculating their respective expectation value (trustworthiness score) and combining it using operators from the computational trust model. We evaluate the method using an automotive use case, i.e. over-the-air (OTA) update software. We describe a possible integration of the proposed method into a solution which would be valuable for cybersecurity stakeholders, e.g. cybersecurity managers, cybersecurity architects and software quality managers, aiming to monitor security health of automotive software throughout its development life cycle.