# Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing

arXiv: https://arxiv.org/abs/2603.11433 · HTML: https://mlsanity.com/paper/2603.11433/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://mlsanity.com/paper/1711.00832/) (2017)
- [What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study](https://mlsanity.com/paper/2006.05990/) (2020)
- [Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning](https://mlsanity.com/paper/1906.08805/) (2019)
- [Detection and Mitigation of Attacks on Transportation Networks as a Multi-Stage Security Game](https://mlsanity.com/paper/1808.08349/) (2018)
- [Road Traffic Poisoning of Navigation Apps: Threats and Countermeasures](https://mlsanity.com/paper/2002.05051/) (2020)
- [Traffic networks are vulnerable to disinformation attacks](https://mlsanity.com/paper/2003.03723/) (2020)
- [Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks](https://mlsanity.com/paper/2312.14625/) (2023)
- [Strategic Information Attacks on Incentive-Compatible Navigational Recommendations in Intelligent Transportation Systems](https://mlsanity.com/paper/2310.01646/) (2023)
- [Sensing Resource Allocation Against Data-Poisoning Attacks in Traffic Routing](https://mlsanity.com/paper/2404.02876/) (2024)

## Abstract (arXiv)

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.