ADSecLang: a Domain-Specific Language for Cybersecurity Testing of Autonomous Vehicles

Domain-specific languages for safety validation testing have reduced the complexity of safety scenario generation and enhanced the adoption of Autonomous Driving (AD) safety testing. Yet, there is a lack of comparable solutions for cybersecurity testing. In this work, we present ADSecLang, a domain-specific language for cybersecurity testing of AD systems. ADSecLang provides a concise syntax that enables the tester to construct scenarios for AD cybersecurity straightforwardly that can be implemented in an AD test simulation platform. The proposed language is validated within the CARLA AD simulation platform in a use-case scenario of two diverse AV camera sensor manipulation attacks on a state-of-the-art trajectory-guided AD solution. The results show that the language is able to support the translation of threats from an abstract description to the technical implementation of the attack test cases and that these test cases could identify vulnerabilities in the target AD solution.