Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
arXiv:2603.11433v1 Announce Type: new Abstract: In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To address these threats, we formulate a strategically zero-sum game between an attacker, who injects such perturbations, and a defender, who detects anomalies based on the observed travel times of network edges. We propose a computational method based on multi-agent reinforcement learning to compute a Nash equilibrium of this game, providing an optimal detection strategy, which ensures that total travel time remains within a worst-case bound, even in the presence of an attack. We present an extensive experimental evaluation that demonstrates the robustness and practical benefits of our approach, providing a powerful framework to improve the resilience of
arXiv:2603.11433v1 Announce Type: new Abstract: In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To address these threats, we formulate a strategically zero-sum game between an attacker, who injects such perturbations, and a defender, who detects anomalies based on the observed travel times of network edges. We propose a computational method based on multi-agent reinforcement learning to compute a Nash equilibrium of this game, providing an optimal detection strategy, which ensures that total travel time remains within a worst-case bound, even in the presence of an attack. We present an extensive experimental evaluation that demonstrates the robustness and practical benefits of our approach, providing a powerful framework to improve the resilience of transportation networks against false data injection. In particular, we show that our approach yields approximate equilibrium policies and significantly outperforms baselines for both the attacker and the defender.
Executive Summary
This article proposes an adversarial reinforcement learning framework to detect false data injection attacks in vehicular routing systems. The framework models a strategically zero-sum game between an attacker and a defender, leveraging multi-agent reinforcement learning to compute a Nash equilibrium and provide an optimal detection strategy. Experimental evaluation demonstrates the robustness and practical benefits of the approach, yielding approximate equilibrium policies and outperforming baselines for both the attacker and defender. The framework aims to improve transportation network resilience against false data injection attacks, which can mislead vehicles toward suboptimal routes and increase congestion.
Key Points
- ▸ Proposed an adversarial reinforcement learning framework for detecting false data injection attacks in vehicular routing systems
- ▸ Modeled a strategically zero-sum game between an attacker and a defender
- ▸ Employed multi-agent reinforcement learning to compute a Nash equilibrium and provide an optimal detection strategy
Merits
Robustness and Practical Benefits
The proposed framework demonstrates robustness and practical benefits in detecting false data injection attacks, improving transportation network resilience
Approximate Equilibrium Policies
The approach yields approximate equilibrium policies, enabling effective detection and mitigation of false data injection attacks
Outperformance of Baselines
The framework outperforms existing baselines for both the attacker and defender, showcasing its efficacy in addressing false data injection threats
Demerits
Complexity of Implementation
The proposed framework may require significant computational resources and expertise to implement, potentially limiting its adoption
Assumptions and Simplifications
The framework relies on simplifying assumptions and may not account for all complexities and nuances of real-world transportation networks
Scalability and Generalizability
The framework's performance and effectiveness may degrade as the size and complexity of the transportation network increase
Expert Commentary
The article presents a novel and comprehensive approach to detecting false data injection attacks in vehicular routing systems. The proposed framework leverages adversarial reinforcement learning to model a strategically zero-sum game between an attacker and a defender, providing an optimal detection strategy. While the framework demonstrates robustness and practical benefits, its implementation may require significant computational resources and expertise. The framework's assumptions and simplifications also limit its applicability to real-world transportation networks. Nevertheless, the article provides a valuable contribution to the field of transportation systems security, highlighting the role of AI and ML in addressing cybersecurity threats.
Recommendations
- ✓ Future research should focus on developing more scalable and generalizable frameworks that can account for complexities and nuances of real-world transportation networks
- ✓ The proposed framework should be further evaluated in realistic scenarios and environments to assess its performance and effectiveness