VMGuard: Reputation-Based Incentive Mechanism for Poisoning Attack Detection in Vehicular Metaverse

TL;DR

VMGuard is a reputation-based security framework designed to detect and prevent data poisoning attacks in the vehicular Metaverse by assessing trustworthiness of sensing devices through user feedback and subjective logic.

Contribution

This paper introduces VMGuard, a novel four-layer security framework with a reputation-based incentive mechanism for safeguarding vehicular Metaverse systems from poisoning attacks.

Findings

Effectively prevents poisoning attacks by malicious SIoT devices.

Ensures reliable SIoT devices are not falsely excluded.

Validated through comprehensive simulations showing improved security.

Abstract

The vehicular Metaverse represents an emerging paradigm that merges vehicular communications with virtual environments, integrating real-world data to enhance in-vehicle services. However, this integration faces critical security challenges, particularly in the data collection layer where malicious sensing IoT (SIoT) devices can compromise service quality through data poisoning attacks. The security aspects of the Metaverse services should be well addressed both when creating the digital twins of the physical systems and when delivering the virtual service to the vehicular Metaverse users (VMUs). This paper introduces vehicular Metaverse guard (VMGuard), a novel four-layer security framework that protects vehicular Metaverse systems from data poisoning attacks. Specifically, when the virtual service providers (VSPs) collect data about physical environment through SIoT devices in the field, the delivered content might be tampered. Malicious SIoT devices with moral hazard might have private incentives to provide poisoned data to the VSP to degrade the service quality (QoS) and user experience (QoE) of the VMUs. The proposed framework implements a reputation-based incentive mechanism that leverages user feedback and subjective logic modeling to assess the trustworthiness of participating SIoT devices. More precisely, the framework entails the use of reputation scores assigned to participating SIoT devices based on their historical engagements with the VSPs. Ultimately, we validate our proposed model using comprehensive simulations. Our key findings indicate that our mechanism effectively prevents the initiation of poisoning attacks by malicious SIoT devices. Additionally, our system ensures that reliable SIoT devices, previously missclassified, are not barred from participating in future rounds of the market.