Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models

2025-06-20

AI Summary

To address the growing sophistication of cyberattacks against Intelligent Transportation Systems (ITS) and the fragmentation between traffic simulation and cybersecurity assessment, this paper proposes the first LLM-driven multi-agent collaborative framework that unifies traffic simulation and cyber-attack-defense闭环 modeling. The framework integrates large language models (LLMs), multi-agent systems, SUMO/VISSIM traffic simulation interfaces, and a cybersecurity policy generation module to enable automated attack scenario generation, quantitative impact assessment, and co-design of defensive mechanisms. Its key innovation lies in deep embedding of LLMs throughout the end-to-end ITS attack-defense modeling pipeline, overcoming limitations of traditional static testing. Empirical evaluation on vehicle-infrastructure cooperative broadcast attacks demonstrates precise quantification of a 10.2% increase in average travel time due to attack injection; the proposed defense mechanism reduces this increment by 3.3 percentage points, significantly enhancing both cybersecurity resilience and simulation fidelity of ITS.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Enhancing cybersecurity in Intelligent Transportation Systems using LLMs
• Automating traffic scenario creation and cyberattack defense strategies
• Mitigating traffic delays caused by cyberattacks on connected vehicles

Innovation

• LLM-driven multi-agent traffic simulation framework
• Automated cyberattack and defense strategy design
• Scalable approach for transportation cybersecurity