This E&T Innovation Award recognises an organisation which is taking proactive steps to counter attacks and take preventative measures to remain one-step ahead of cyber threats.
Mobius, began in January 2020 as a combination of traffic simulation and web-based geospatial analytics. It was developed with the aim of enabling anomaly detection in a population of mixed manual, semi-autonomous and autonomous vehicles. The aim is to assist in identifying vehicles that may have become compromised as well as supporting planning and decision-making processes.
“Mobius is a great tool to demonstrate our innovation for improving the security of physical assets both mobile such as self-driving cars and drones or fixed such as our buildings and network equipment. This award shows that we are at the forefront of security research and it’s a great testament to the work that Jonathan and the team have done over the last few months.” Ben Azvine, Head of security research at Adastral Park.
Mobius enables complex modelling of cyber physical traffic incidents using urban traffic data and vehicle information. The severity of malware incidents can be controlled through parameters, which simulate the impact of various types of malware. The spread of malware over V2X communications is modelled through traditional epidemiological models, incorporating the susceptibility of individual vehicles to infection and parametrically altering the rate of infection spread through traffic over a period of time.
As we migrate to future CAV systems, the number of computing and network components will dramatically increase. Both internal to vehicles, from vehicles to base stations and between vehicles on the road. The security envelope and attack surface is thus no longer restricted to the vehicles manufactured on-board systems. In this emerging CAV environment, we will see the spread of malicious code between vehicles, and base stations, in a similar manner to the spread of biological pathogens. Indeed, recent research at BT has demonstrated how simulations using such models can help accurately predict the spread of malware across large-scale computing networks.
Mobius helps identifying malware-compromised vehicles and support planning and decision-making processes for the world of CAVs.
We are actively employing Mobius for: malware propagation, understanding CAV and traditional vehicle interactions, fleet organisation, civic planning and unmanned traffic control. The geospatial capabilities have also been used to help monitor infrastructure against physical attack.
There is a key role for vehicle simulation software capable of modelling cyber threats to assist with threat analysis and decision making for highway authorities, OEMs and fleet operators, amongst others. Mobius is a major step towards being prepared for future threats.