# Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks

arXiv: https://arxiv.org/abs/2312.14625 · HTML: https://mlsanity.com/paper/2312.14625/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [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)

## Built on by (most cited first, 2 of 2)

- [Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing](https://mlsanity.com/paper/2405.13205/) (2024)
- [Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing](https://mlsanity.com/paper/2603.11433/) (2026)

## Abstract (arXiv)

The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors. Adversaries may exploit vulnerabilities in the data collection or processing of navigation services to inject false information, and to thus interfere with the drivers' route selection. Such attacks can significantly increase traffic congestions, resulting in substantial waste of time and resources, and may even disrupt essential services that rely on road networks. To assess the threat posed by such attacks, we introduce a computational framework to find worst-case data-injection attacks against transportation networks. First, we devise an adversarial model with a threat actor who can manipulate drivers by increasing the travel times that they perceive on certain roads. Then, we employ hierarchical multi-agent reinforcement learning to find an approximate optimal adversarial strategy for data manipulation. We demonstrate the applicability of our approach through simulating attacks on the Sioux Falls, ND network topology.