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

2024-12-28

AI Summary

To address DDoS attacks and malicious roadside unit (RSU) interference threatening AI agents’ migration across RSUs in vehicular metaverses—induced by vehicle mobility—this paper formulates the migration decision problem as a Partially Observable Markov Decision Process (POMDP) for the first time. We propose a robust migration policy based on Multi-Agent Proximal Policy Optimization (MAPPO) and design a lightweight, dynamic RSU trust evaluation mechanism to detect malicious nodes in real time. The approach reduces overall migration latency by 43.3% while ensuring migration security, thereby significantly enhancing the timeliness and reliability of virtual services. Key contributions include: (i) a POMDP-based migration modeling framework; (ii) a MAPPO-driven secure collaborative decision-making mechanism; and (iii) an online trust evolution model tailored to vehicular edge computing environments.

Technology Category

• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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%.

Problem

• Cybersecurity
• AI Assistants
• Metaverse

Innovation

• AI companions protection
• DDoS counteract algorithms
• trust system for RSUs