# Detecting Malicious Accounts in Web3 through Transaction Graph

arXiv: https://arxiv.org/abs/2410.20713 · HTML: https://mlsanity.com/paper/2410.20713/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2024.

## Built on by (most cited first, 5 of 5)

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- [UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis](https://mlsanity.com/paper/2508.01343/) (2025)
- [Towards Understanding Deep Learning Model in Image Recognition via Coverage Test](https://mlsanity.com/paper/2505.08814/) (2025)
- [Facial Recognition Leveraging Generative Adversarial Networks](https://mlsanity.com/paper/2505.11884/) (2025)

## Abstract (arXiv)

The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. The current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. In this paper, we present ScamSweeper, a novel framework to identify web3 scams on Ethereum. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts. Our experiments indicate that ScamSweeper exceeds the state-of-the-art in detecting web3 scams.