Use of Vehicle Cyber Engineering (VCE) testbeds to develop AI Safety enhancements based on ISO TR 5469.

The University of Detroit-Mercy (UD-M) has developed CLaaS (Cyber-Security Labs as a Service) to support teaching students how to understand and mitigate cyber security attacks. The UD-M Vehicle Cyber Engineering (VCE) laboratory has physical hardware testbeds to support education and research on actual vehicle systems. With the increased use of Machine Learning (ML) and Artificial Intelligence (AI) in on- and offroad vehicles, it is becoming critical to enhance the laboratory to help educate a new generation of engineers that understand the cyber security and functional implications of AI/ML. The ISO TR 5469 Technical Report provides a framework to classify the AI/ML technology based on usage level and the properties and requirements to mitigate cyber and functional safety risks for the technology. This paper provides an overview of the approach used by ISO TR 5469 as well as an example how one of the six ISO TR 5469 desirable properties (resilience to adversarial and intentional malicious input) can be analyzed for adversarial attacks. This paper will also show how the VCE testbed can be used to provide a student with a trained AI model where the student can simulate a non-targeted attack. The testbed can be used to simulate a poisoning attack where the student can manipulate a training data set to deceive the AI model during a simulated deployment.