  The rise of connected and autonomous vehicles, combined with the
proliferation of IoT and connected surfaces, lead to the emergence of novel
complex cyber risks. Lack of encryption and authentication in internal
vehicular networks are widely recognized as cause for concern by cybersecurity
experts, automobile, and OEM manufacturers. This concern has only been growing
with the increase in cybersecurity incidents and demonstrations showing
different vehicular vulnerabilities, making it nearly impossible to completely
secure vehicles against cyber-attacks. Of particular concern is the potential
for large-scale vehicular cyber-attacks to cascade to transportation networks,
which are the lifeline of cities. Here, we develop a framework based on complex
network theory, traffic flow, and new data based technologies to quantify the
vulnerability of city-scale transportation to cyber-attacks. Application of our
framework to the road network of Boston reveals that targeted attacks on a
small fraction of nodes leads to disproportionately larger disruptions of
routes. We develop an early-detection framework to quantify real-time risk
based on gathering multidimensional traffic flow, incident, and social media
data sets. Our results illustrate an effects based approach to transportation
cyber-defense, through informed, intelligent vehicular agents.

Physics and Society;Soft Condensed Matter