Attack-Resistant Trust Management for Securing Connected Traffic Infrastructures

This research presents a sophisticated, simulation-based cybersecurity framework designed to detect and mitigate cyberattacks targeting urban traffic management infrastructures and interconnected vehicular networks. Utilizing a realistic, high-fidelity simulation of Daytona Beach, Florida, the methodology integrates Raspberry Pi virtual machines as traffic controllers, OPNSense firewalls for network vulnerability simulation, SUMO for detailed vehicular mobility modeling, and Metasploit for penetration testing. Among various statistical and deep learning approaches evaluated, Random Forest and Convolutional Neural Networks (CNNs) demonstrated the highest accuracy and robustness in detecting anomalies from traffic-flow data. Employing Explainable AI (XAI) techniques, including Occlusion Sensitivity, LIME, and SHAP, the research team identified critical indicators—specifically, Longest Stop Duration and Total Jam Distance—as key markers of compromised traffic signals. The resulting scalable and interpretable cybersecurity solutions align with strategic USDOT and CYBER-CARE objectives, offering transportation stakeholders actionable tools for enhancing resilience against evolving cyber threats.

• Supplemental Notes:
  • This material is based on work supported by the U.S. Department of Transportation, OST-R, University Transportation Center Program, the USDOT Tier 1 UTC Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE).

Language

• English

Project

• Status: Active
• Funding: $100,000.00
• Contract Numbers:

  69A3552348332

• 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:

  Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)

  University of Houston
  Houston, TX  United States 
• Project Managers:

  Zhang, Yunpeng

  Kline, Robin

• Performing Organizations:

  Embry-Riddle Aeronautical University

  600 S. Clyde Morris Boulevard
  Daytona Beach, Fl  United States  32114
• Principal Investigators:

  Liu, Yongxin

• Start Date: 20240101
• Expected Completion Date: 20260101
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Source Data: https://uh.edu/cybercare/research/projects/c23c06/

Subject/Index Terms

• TRT Terms: Artificial intelligence; Computer security; Risk assessment; Simulation; Traffic signals; Urban areas; Vehicle to infrastructure communications
• Geographic Terms: Daytona Beach (Florida)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Security and Emergencies;

Filing Info

• Accession Number: 01953915
• Record Type: Research project
• Source Agency: Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)
• Contract Numbers: 69A3552348332
• Files: UTC, RIP, STATEDOT
• Created Date: Apr 29 2025 4:39PM