EPSRC Centre for Doctoral Training in Cyber Security

 

The future lies in smart cities where we have technology improving andfacilitating citizens daily lives. Smart cities are a highly connected environment, which includes mobile devices, sensors around the city and services in the cloud. All of these components contribute data to big data analytics that use machine learning to bring value to the citizens of smart cities. Autonomous vehicles have also benefited from the evolution of machine learning, enabling functions such as the real time perception of hazards on the road, analysis of sensor data to prevent collisions and theoptimisation of route planning. The future of autonomous vehicles will be connected and cooperative so that they benefit from the rich information systems around them to assist in decision making. This will provide benefits in time efficiency, money and most importantly improvement in safety. Nevertheless, the inclusion of machine learning technologies also provides another attack vector for adversaries.

 

Attacks on machine learning will have serious consequences for connected autonomous vehicles regarding safety of passengers and pedestrians in addition to impacts on businesses and services utilising these vehicles. We therefore must understand how the resilience of the connected autonomous vehicle networks are impacted by attacks on machine learning. By considering connected autonomous vehicle networks as a complex dynamic and connected system of machine learning models, we can investigate the possible cascading impact of attacks to aid architects in understanding the areas of weakness in these systems.