Cyber Resilience of Connected and Autonomous Transportation Systems (Phase II): Game-theoretic Security Assurance of Network-level Traffic Signal Control

Two trends are transforming transportation systems. On the bright side, the increasing complexity of cyber-physical technological advances enable connectivity and autonomy through seamless integration of computation, communication, sensing, and control, holding promise for societal and economic impacts. On the dark side, however, these tightly coupled cyber-physical interdependencies can be self-defeating as they can pose new vulnerabilities to accelerating disruptions in cyber space (i.e., cyber-attacks), raising concerns about the safety, security, and privacy of the transportation system users. In view of these two overlapping trends, it is crucial to bolstering the transportation cyber-physical resilience. Building on the findings from a comprehensive literature review of transportation cyber resilience in Phase I of this project, Phase II focuses on security assurance for network-level (connected) and artificial intelligence (AI)-powered (autonomous) traffic signal control systems. Learning-based control methods, such as deep reinforcement learning (DRL), have gained traction in traffic signal control due to their computational capability to efficiently deal with real-time changes in traffic flow dynamics. Despite growing popularity, DRL has been recently found to be vulnerable to adversarial attacks, which precludes its use in safety-critical traffic signal control applications unless its vulnerabilities are mitigated. From a methodological standpoint, mitigating the security issues facing DRL is still a daunting if not impossible task. The well-established methods in the area of (un)supervised machine learning security do not methodologically suit DRL security, since DRL deals with sequential decision-making, i.e., non-fixed distribution of training data, based on interactions with an environment. The interdependence of the current decision on the ones taken in previous time steps increases the degrees-of-freedom of adversarial attacks on DRL, e.g., an adversary can perturb multiple state observations of the environment over time. Given the above research gap in DRL security assurance, most relevant studies resort to empirical (aka heuristic) methods, in which attack models sample only some of the possible disturbances (e.g., self-crafted perturbed data) and the devised defense strategies do not generalize out of the experienced attacks. Leaping from empirical security toward certified security, this project proposes a game-theoretic approach to robustifying DRL-based traffic signal control at the network level. The game-theoretic approach provides computational security assurance through explicitly modeling an adversary’s destabilizing perturbations rather than sampling only some of the possible perturbations. The network-level scope of the proposed project prevents higher-order cyber risks compared to isolated traffic signal control, through the explicit consideration of the underlying network effects, such as inter-agent (intersection) interactions over networks.

• Record URL:
  • https://ppms.cit.cmu.edu/projects/detail/616

Language

• English

Project

• Status: Completed
• Funding: $87,008.00
• Contract Numbers:

  69A3552344811

  69A3552348316

• 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:

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213

  Carnegie Mellon University

  Pittsburgh, PA  United States 
• Performing Organizations:

  University of Texas Rio Grande Valley

  1201 W. University Dr
  Edinburg, TX  United States  78539
• Principal Investigators:

  Noruzoliaee, Mohamadhossein

• Start Date: 20250701
• Expected Completion Date: 20260831
• Actual Completion Date: 20260831
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Computer security; Cyberattacks; Game theory; Machine learning; Traffic signal control systems
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 02002862
• Record Type: Research project
• Source Agency: Safety21 University Transportation Center
• Contract Numbers: 69A3552344811, 69A3552348316
• Files: UTC, RIP
• Created Date: Sep 18 2026 10:00AM