Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins

Ravi Prakash, Tony Thomas

TL;DR

The paper tackles counterfeit NFT-backed digital twins in the industrial metaverse by proposing a hybrid deep-learning pipeline that encodes DT behavioural patterns with a denoising autoencoder (DAE) and classifies them with a Bi-GRU, enabling real-time originality verification. It introduces dynamic metadata managed by AI-driven smart contracts to mitigate clone attacks and authenticates NFT-DTs through encoded behavioural patterns rather than static metadata. Experiments on heater DTs using a Kaggle dataset demonstrate high classification accuracy (≈98%) and robust OOC detection with a threshold-based approach, achieving up to 97.73% accuracy and low false rejection rates. The work contributes a practical security blueprint for NFT-DTs that combines pattern-based verification, dynamic metadata, and blockchain-enabled smart contracts to strengthen NFT-based assets in the IMV, with future avenues including one-class/federated strategies and broader OOC data.

Abstract

The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse.

Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins

TL;DR

The paper tackles counterfeit NFT-backed digital twins in the industrial metaverse by proposing a hybrid deep-learning pipeline that encodes DT behavioural patterns with a denoising autoencoder (DAE) and classifies them with a Bi-GRU, enabling real-time originality verification. It introduces dynamic metadata managed by AI-driven smart contracts to mitigate clone attacks and authenticates NFT-DTs through encoded behavioural patterns rather than static metadata. Experiments on heater DTs using a Kaggle dataset demonstrate high classification accuracy (≈98%) and robust OOC detection with a threshold-based approach, achieving up to 97.73% accuracy and low false rejection rates. The work contributes a practical security blueprint for NFT-DTs that combines pattern-based verification, dynamic metadata, and blockchain-enabled smart contracts to strengthen NFT-based assets in the IMV, with future avenues including one-class/federated strategies and broader OOC data.

Abstract

The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse.

Paper Structure

This paper contains 20 sections, 7 equations, 7 figures, 7 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Industrial Metaverse
  4. NFTs in Metaverse
  5. Behavioural Pattern in Metaverse
  6. Methodology
  7. Pattern Encoding
  8. Behavioural Pattern Classification
  9. Experimental Setup
  10. Experimental Set-up for DT Behavioural Pattern Recognition
  11. Dataset
  12. Security Analysis
  13. Issues with DT Cloning
  14. Limitations of existing DT clone detection method
  15. Securing the Behavioural Patterns
  16. ...and 5 more sections

Figures (7)

  • Figure 1: Components of Industrial Metaverse
  • Figure 2: DT classification from raw behavioural patterns
  • Figure 3: Data flow in a Bi-GRU
  • Figure 4: DT verification based on the pattern classification
  • Figure 5: Features of the test bed
  • ...and 2 more figures