Attack Data Generation Framework for Autonomous Vehicle Sensors

Driving scenarios of autonomous vehicles combine many data sources with new networking requirements in highly dynamic system setups. To keep security mechanisms applicable to new application fields in the automotive domain, our work introduces a security framework to generate, attack, and validate realistic data sets at rest and in transit. Concerning realistic data sets, our framework leverages autonomous driving simulators as well as static data sets of vehicle sensors. A configurable networking setup enables flexible data encapsulation to perform and validate networking attacks on data in transit. We validate our results with intrusion detection algorithms and simulation environments. Generated data sets and configurations are reproducible, portable, storable, and support iterative security testing of scenarios.