Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models

TL;DR

This paper introduces a multi-agent framework utilizing Large Language Models to simulate traffic scenarios, test cyberattack strategies, and develop defenses, significantly improving cybersecurity resilience in Intelligent Transportation Systems.

Contribution

It presents a novel LLM-based multi-agent framework for traffic simulation and cybersecurity testing, enabling automated scenario creation, attack design, and defense development.

Findings

10.2% increase in travel time during attack

3.3% reduction in delays with defense strategy

Demonstrates LLMs' potential in transportation cybersecurity

Abstract

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.