Abstract
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 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.
Description
Timeline
Strategic Description / RD&T
Section left blank until USDOT’s new priorities and RD&T strategic goals are available in Spring 2026.
Deployment Plan
Expected Outcomes/Impacts
The expected outcome of this research project is the capability enhancement in enhancing transportation safety through bolstering the cyber resilience of connected and autonomous transportation systems, exemplified through the context of network-level (connected) and AI-powered (autonomous) traffic signal control systems.
The anticipated impacts of this research project are 1) improved understanding of security assurance for safety-critical traffic signal control at the network scale; and 2) enlargement of the pool of trained transportation professionals at the nexus of transportation safety and cyber resilience.
Expected Outputs
The anticipated outputs of this research project include a game-theoretic data-driven model for adversarially robust network-level traffic signal control as well as model implementation results.
TRID
The security and resilience of cyber-physical systems have garnered attentions only in recent years due to the growing frequency and complexity of cyber-attacks caused in part by the increasing digitalization of critical infrastructure systems (e.g., transportation). Despite its rising popularity and national priority, the field is still at its infancy and can significantly benefit from timely and comprehensive reviews of the state-of-the-art and departures therefrom. While offering valuable insights, the small number of existing review studies on this topic, which mainly appear in the transportation science and computer science communities, do not comprehensively cover some of the urgent and emerging issues arising in this area. Examples of underexplored aspect of cyber-physical resilience include, but are not limited to, 1) a lifecycle approach (i.e., pre-disruption prevention and post-disruption response and recovery) by focusing on the corresponding lifecycle-based concept of cyber resilience rather than the pre-disruption notion of cybersecurity, 2) a theoretical approach to not only bolster resilience through advanced computation, but also provide theoretical guarantees for the worst-case system performance under certain cyber-attacks, and 3) a system-of-systems approach to prevent cascading failures in interdependent infrastructure systems.
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| h.noruzoliaee@utrgv.edu |
Noruzoliaee, Mohamadhossein |
University of Texas - Rio Grande Valley |
PI |
Faculty - Untenured, Tenure Track |
Budget
Amount of UTC Funds Awarded
$32431.00
Total Project Budget (from all funding sources)
$87008.00
Documents
Match Sources
No match sources!
Partners
| Name |
Type |
| University of Texas - Rio Grande Valley |
Deployment Partner_ Deployment Partner_ |
