# Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models

arXiv: https://arxiv.org/abs/2506.16699 · HTML: https://mlsanity.com/paper/2506.16699/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2025.

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

- [Cybersecurity-Focused Anomaly Detection in Connected Autonomous Vehicles Using Machine Learning](https://mlsanity.com/paper/2506.22984/) (2025)
- [Developing an AI Assistant for Knowledge Management and Workforce Training in State DOTs](https://mlsanity.com/paper/2603.03302/) (2026)

## Abstract (arXiv)

Intelligent Transportation Systems (ITS) are increasingly vulnerable to sophisticated cyberattacks due to their complex, interconnected nature. Ensuring the cybersecurity of these systems is paramount to maintaining road safety and minimizing traffic disruptions. This study presents a novel multi-agent framework leveraging Large Language Models (LLMs) to enhance traffic simulation and cybersecurity testing. The framework automates the creation of traffic scenarios, the design of cyberattack strategies, and the development of defense mechanisms. A case study demonstrates the framework's ability to simulate a cyberattack targeting connected vehicle broadcasts, evaluate its impact, and implement a defense mechanism that significantly mitigates traffic delays. Results show a 10.2 percent increase in travel time during an attack, which is reduced by 3.3 percent with the defense strategy. This research highlights the potential of LLM-driven multi-agent systems in advancing transportation cybersecurity and offers a scalable approach for future research in traffic simulation and cyber defense.