Development of Real-time Cyberattack Prediction & Warning System

With the increasing prevalence of cyberattacks targeting transportation systems, there is a critical need for a proactive framework capable of predicting potential cyber threats and issuing timely warnings. This study introduces Cyber-TFWS, a trajectory-based forecasting and warning system designed to enhance connected vehicle (CV) safety under spoofing cyberattacks. By leveraging deep learning-based forecasting models, Cyber-TFWS predicts vehicle trajectories under attack, enabling early detection and effective mitigation strategies. The research involves a detailed literature review and the implementation of a generative adversarial model (CAGAN) for trajectory prediction. Key findings demonstrate that Cyber-TFWS significantly improves traffic safety, successfully preventing 100% red-light running incidents under specific perception-reaction time (PRT) conditions. The study also highlights the role of acceleration in improving prediction accuracy and identifies challenges in forecasting trajectories with abrupt velocity changes. Extensive simulations validate the system's robustness, underscoring its potential for real-world deployment in securing intelligent transportation systems against cyber threats.

• Record URL:
  • https://uh.edu/cybercare/research/projects/c23c17/
• 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: $75,000.00
• 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

• Performing Organizations:

  University of Cincinatti

  Cincinatti, OH  United States  45221
• Principal Investigators:

  Li, Zhixia

• Start Date: 20230701
• Expected Completion Date: 20260630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Computer security; Connected vehicles; Deep learning; Detection and identification systems; Red light running; Traffic safety; Vehicle trajectories; Warning systems
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01953957
• 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 30 2025 4:06PM