Hanna Kim (KAIST), Jian Cui (Indiana University Bloomington), Eugene Jang (S2W Inc.), Chanhee Lee (S2W Inc.), Yongjae Lee (S2W Inc.), Jin-Woo Chung (S2W Inc.), Seungwon Shin (KAIST)

As Non-Fungible Tokens (NFTs) continue to grow in popularity, NFT users have become targets of phishing scammers, called 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 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 DRAINCLoG under a wide variety of evasion attacks.

View More Papers

Binary Code Patching: An Ancient Art Refined for the...

Dr. Barton P. Miller (Vilas Distinguished Achievement Professor at The University of Wisconsin-Madison)

Read More

GhostType: The Limits of Using Contactless Electromagnetic Interference to...

Qinhong Jiang (Zhejiang University), Yanze Ren (Zhejiang University), Yan Long (University of Michigan), Chen Yan (Zhejiang University), Yumai Sun (University of Michigan), Xiaoyu Ji (Zhejiang University), Kevin Fu (Northeastern University), Wenyuan Xu (Zhejiang University)

Read More

WIP: A First Look At Employing Large Multimodal Models...

Mohammed Aldeen, Pedram MohajerAnsari, Jin Ma, Mashrur Chowdhury, Long Cheng, Mert D. Pesé (Clemson University)

Read More

Exploiting Diagnostic Protocol Vulnerabilities on Embedded Networks in Commercial...

Carson Green, Rik Chatterjee, Jeremy Daily (Colorado State University)

Read More