DRAINCLoG: Detecting Rogue Accounts with Illegally-obtained NFTs using Classifiers Learned on Graphs

TL;DR

This paper studies NFT drainers' behaviors and introduces DRAINCLoG, a graph neural network-based system that detects rogue accounts involved in illegal NFT transactions with high accuracy.

Contribution

It is the first comprehensive analysis of NFT drainers' behaviors and the first dedicated detection system using graph neural networks for this purpose.

Findings

DRAINCLoG achieves high detection precision on real-world data.

NFT drainers exhibit distinct transaction and social behaviors.

The system remains robust under various evasion attacks.

Abstract

As Non-Fungible Tokens (NFTs) continue to grow in popularity, NFT users have become targets of phishing attacks by cybercriminals, called \textit{NFT drainers}. Over the last year, \$100 million worth of NFTs were stolen by drainers, and their presence remains a serious threat to the NFT trading space. However, no work has yet comprehensively investigated the behaviors of drainers in the NFT ecosystem. In this paper, we present the first study on the trading behavior of NFT drainers and introduce the first dedicated NFT drainer detection system. We collect 127M NFT transaction data from the Ethereum blockchain and 1,135 drainer accounts from five sources for the year 2022. We find that drainers exhibit significantly different transactional and social contexts from those of regular users. With these insights, we design \textit{DRAINCLoG}, an automatic drainer detection system utilizing Graph Neural Networks. This system effectively captures the multifaceted web of interactions within the NFT space through two distinct graphs: the NFT-User graph for transaction contexts and the User graph for social contexts. Evaluations using real-world NFT transaction data underscore the robustness and precision of our model. Additionally, we analyze the security of \textit{DRAINCLoG} under a wide variety of evasion attacks.