Cybersecurity of Connected and Automated Vehicles via Traffic Anomaly Detection

Connected and automated vehicles (CAVs) provide new opportunities for malicious actors to compromise vehicle security and compromise traffic flow. While obvious hacks that cause crashes may be easy to identify and isolate, other vehicle compromises may be more difficult to identify, especially if the hack impacts vehicle driving behavior or causes a vehicle to transmit faulty data via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) connectivity. In this work, the research team proposes to previous work funded by a University of Minnesota Center for Transportation Studies seed grant which conducted trajectory anomaly detection in compromised AVs (without connectivity) to consider potential data anomalies in a connected vehicle network and identify compromised vehicles on their driving behavior and the data they are sharing across the communication network (e.g., 5G connected vehicles). Specifically, the team proposes to use car following models to simulate traffic flow both of typical mixed autonomy traffic as well as traffic where some of the automated vehicles have been compromised and are sending compromised communications to other vehicles. The communication layer will also be modeled independently, with vehicles sharing basic safety messages (BSMs) across the network. Potential cyberattacks will be implemented in simulation, where compromised messages are communicated across the network, and the resulting traffic and communication data as well as traffic and communication data from uncompromised traffic flow will be compared to understand the potential impact of such attacks. Furthermore, the generated synthetic data will be used to develop anomaly detection techniques that leverage advancements in neural networks and autoencoders to identify atypical traffic and communication data.

• Record URL:
  https://ccat.umtri.umich.edu/research/minnesota/cybersecurity-of-connected-and-automated-vehicles-via-traffic-anomaly-detection/

Language

• English

Project

• Status: Active
• Funding: $73,079 Federal; $73079 Cost Share; $146,158 Total
• Contract Numbers:

  69A3552348305

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of Michigan Transportation Research Institute

  2901 Baxter Road
  Ann Arbor, Michigan  United States  48109
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of Minnesota, Minneapolis

  Center for Transportation Studies
  Minneapolis, MN  United States 
• Principal Investigators:

  Stern, Raphael

• Start Date: 20240101
• Expected Completion Date: 20250531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Car following; Computer security; Connected vehicles; Mobile communication systems; Traffic flow; Traffic simulation
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01905388
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3552348305
• Files: UTC, RIP
• Created Date: Jan 23 2024 5:16PM