Defending Against Network Attacks for Secure AI Agent Migration in Vehicular Metaverses

TL;DR

This paper addresses security challenges in AI agent migration within vehicular metaverses by modeling attacks and proposing reinforcement learning and trust mechanisms, significantly reducing migration latency.

Contribution

It introduces a novel security framework combining POMDP modeling, multi-agent reinforcement learning, and trust assessment to defend against network attacks during AI agent migration.

Findings

Effective defense against DDoS and malicious RSU attacks.

Reduces AI agent migration latency by approximately 43.3%.

Validates proposed solutions through numerical simulations.

Abstract

Vehicular metaverses, blending traditional vehicular networks with metaverse technology, are expected to revolutionize fields such as autonomous driving. As virtual intelligent assistants in vehicular metaverses, Artificial Intelligence (AI) agents powered by large language models can create immersive 3D virtual spaces for passengers to enjoy on-broad vehicular applications and services. To provide users with seamless and engaging virtual interactions, resource-limited vehicles offload AI agents to RoadSide Units (RSUs) with adequate communication and computational capabilities. Due to the mobility of vehicles and the limited coverage of RSUs, AI agents need to migrate from one RSU to another RSU. However, potential network attacks pose significant challenges to ensuring reliable and efficient AI agent migration. In this paper, we first explore specific network attacks including traffic-based attacks (i.e., DDoS attacks) and infrastructure-based attacks (i.e., malicious RSU attacks). Then, we model the AI agent migration process as a Partially Observable Markov Decision Process (POMDP) and apply multi-agent proximal policy optimization algorithms to mitigate DDoS attacks. In addition, we propose a trust assessment mechanism to counter malicious RSU attacks. Numerical results validate that the proposed solutions effectively defend against these network attacks and reduce the total latency of AI agent migration by approximately 43.3%.