Cybersquatting in Web3: The Case of NFT

• URL: http://arxiv.org/abs/2504.13573v1
• Date: Fri, 18 Apr 2025 09:14:59 GMT
• Title: Cybersquatting in Web3: The Case of NFT
• Authors: Kai Ma, Ningyu He, Jintao Huang, Bosi Zhang, Ping Wu, Haoyu Wang,
• Abstract summary: This paper presents the first in-depth measurement study of NFT cybersquatting.<n>By analyzing over 220K NFT collections with over 150M NFT tokens, we have identified 8,019 cybersquatting NFT collections targeting 654 popular NFT projects.<n>Our analysis reveals that these NFT cybersquatting activities have resulted in a significant financial impact, with over 670K victims affected by these scams, leading to a total financial exploitation of $59.26 million.
• Score: 7.84980925537562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cybersquatting refers to the practice where attackers register a domain name similar to a legitimate one to confuse users for illegal gains. With the growth of the Non-Fungible Token (NFT) ecosystem, there are indications that cybersquatting tactics have evolved from targeting domain names to NFTs. This paper presents the first in-depth measurement study of NFT cybersquatting. By analyzing over 220K NFT collections with over 150M NFT tokens, we have identified 8,019 cybersquatting NFT collections targeting 654 popular NFT projects. Through systematic analysis, we discover and characterize seven distinct squatting tactics employed by scammers. We further conduct a comprehensive measurement study of these cybersquatting NFT collections, examining their metadata, associated digital asset content, and social media status. Our analysis reveals that these NFT cybersquatting activities have resulted in a significant financial impact, with over 670K victims affected by these scams, leading to a total financial exploitation of $59.26 million. Our findings demonstrate the urgency to identify and prevent NFT squatting abuses.

Related papers

• Exact Certification of (Graph) Neural Networks Against Label Poisoning [50.87615167799367]
  We introduce an exact certification method for label flipping in Graph Neural Networks (GNNs) We apply our method to certify a broad range of GNN architectures in node classification tasks. Our work presents the first exact certificate to a poisoning attack ever derived for neural networks.
  arXiv  Detail & Related papers  (2024-11-30T17:05:12Z)
• Characterizing the Solana NFT Ecosystem [0.8225825738565354]
  We conduct the first systematic research on the characteristics of Solana NFTs from two perspectives. Investigating users' economic activity and NFT owner information reveals that the top users in Solana NFT are skewed toward a higher distribution of purchases. We employ the Local Outlier Factor algorithm to conduct a wash trading audit on 2,175 popular Solana NFTs.
  arXiv  Detail & Related papers  (2024-03-16T10:16:49Z)
• The Dark Side of NFTs: A Large-Scale Empirical Study of Wash Trading [28.20696034160891]
  We analyze 8,717,031 transfer events and 3,830,141 sale events from 2,701,883 NFTs. We identify three types of NFT wash trading and propose identification algorithms. We also provide insights from six aspects, i.e., marketplace design, profitability, NFT project design, payment token, user behavior, and NFT ecosystem.
  arXiv  Detail & Related papers  (2023-12-19T19:29:24Z)
• SoK: On the Security of Non-Fungible Tokens [9.574922406565372]
  Non-fungible tokens (NFTs) drive the prosperity of the Web3 ecosystem. There is a lack of understanding of kinds of NFT security issues. This paper is the first SoK of NFT security, shedding light their root causes, real-world attacks, and potential ways to address them.
  arXiv  Detail & Related papers  (2023-12-13T09:16:24Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• With Trail to Follow: Measurements of Real-world Non-fungible Token Phishing Attacks on Ethereum [4.297180143942048]
  There has been growing anecdotal evidence that new means of NFT phishing attacks have emerged in ecosystem. Most of the existing research focus on detecting phishing scam accounts for native cryptocurrency on the blockchain. There is a lack of research in the area of phishing attacks of emerging NFTs.
  arXiv  Detail & Related papers  (2023-07-04T09:18:59Z)
• Unveiling the Risks of NFT Promotion Scams [24.54041279375181]
  We study 439 promotion services (accounts) on Twitter that have collectively promoted 823 unique NFT projects. More than 36% of these projects were fraudulent, comprising of phishing, rug pull, and pre-mint scams. We develop a machine learning tool that was able to proactively detect 382 new fraudulent NFT projects on Twitter.
  arXiv  Detail & Related papers  (2023-01-24T04:13:44Z)
• A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain [53.8917088220974]
  The Non-Fungible Token (NFT) market experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022. Concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially. We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774.
  arXiv  Detail & Related papers  (2022-12-02T15:03:35Z)
• Probably Something: A Multi-Layer Taxonomy of Non-Fungible Tokens [62.997667081978825]
  Non-Fungible Tokens (NFTs) are hyped and increasingly marketed as essential building blocks of the Metaverse. This paper aims to establish a fundamental and comprehensive understanding of NFTs by identifying and structuring common characteristics within a taxonomy.
  arXiv  Detail & Related papers  (2022-08-29T18:00:30Z)
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends [115.84291569988748]
  Graph neural networks (GNNs) have emerged as competent graph learning methods for diverse real-world scenarios. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness.
  arXiv  Detail & Related papers  (2022-05-16T02:21:09Z)
• Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks [55.531896312724555]
  Bayesian Networks (BNNs) are robust and adept at handling adversarial attacks by incorporating randomness. We create our BNN model, called BNN-DenseNet, by fusing Bayesian inference (i.e., variational Bayes) to the DenseNet architecture. An adversarially-trained BNN outperforms its non-Bayesian, adversarially-trained counterpart in most experiments.
  arXiv  Detail & Related papers  (2021-11-16T16:14:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.