Explainable Machine Learning for Data Efficient Attack Detection in Intelligent Transportation Systems

The rise of Intelligent Transportation Systems (ITS) and connected autonomous vehicles (CAVs) has revolutionized transportation but has also introduced significant cybersecurity risks. This project focuses on developing an explainable anomaly detection framework that leverages normal operational data to identify cyber-attacks, addressing the challenge of limited labeled attack data in early ITS deployment stages. By framing the problem as open-set recognition, the system integrates explainable artificial intelligence (AI) techniques, such as Occlusion Sensitivity Maps and a zero-bias deep learning framework, to enhance transparency and trust. Incremental learning algorithms will enable data-efficient adaptation to evolve cyber-attack scenarios, ensuring robust protection against threats like compromised nodes and system exploits.

• Record URL:
  • https://uh.edu/cybercare/research/project2/
• 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: $52,000.00
• 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 (Jack)

• Performing Organizations:

  Embry-Riddle Aeronautical University

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

  Liu, Yongxin

• Start Date: 20240701
• Expected Completion Date: 20260630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Computer security; Connected vehicles; Intelligent transportation systems; Machine learning
• Subject Areas: Data and Information Technology; Highways; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01956895
• 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: May 29 2025 10:25PM