Wu, C;
Chen, J;
Li, J;
Xu, J;
Jia, J;
Hu, Y;
Feng, Y;
... Xiang, Y; + view all
(2025)
Profit or Deceit? Mitigating Pump and Dump in DeFi via Graph and Contrastive Learning.
IEEE Transactions on Information Forensics and Security
pp. 1-15.
10.1109/TIFS.2025.3594873.
(In press).
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Abstract
Pump-and-Dump (PD) schemes pose a significant threat to the stability and fairness of Decentralized Finance (DeFi) markets, often resulting in substantial financial losses for investors. The early and accurate detection of these schemes is crucial for preserving trust in the rapidly expanding cryptocurrency ecosystem. However, existing detection methods primarily rely on post-event analysis and heuristic-based approaches, which are often inadequate for real-time and precise identification of PD activities. In this paper, we present PUMPWATCHER, an innovative framework that employs Graph Neural Networks (GNNs) and contrastive learning to detect PD schemes by modeling transaction behaviors within temporal graphs. PUMPWATCHER integrates advanced transaction graph construction, temporal GNNs, and contrastive learning techniques to enhance node and edge representations, thereby improving the detection of intricate and covert PD operations. We validate PUMPWATCHER on a dataset from Uniswap, encompassing 924,508 transactions across 858 tokens within December 2022. The results show that PUMPWATCHER outperforms state-of-the-art models, achieving a superior balanced accuracy of 92.3%, while significantly minimizing false positives and negatives. These outcomes highlight its potential to set a new standard in real-time detection of market manipulation, paving the way for more secure and resilient DeFi ecosystems.
| Type: | Article |
|---|---|
| Title: | Profit or Deceit? Mitigating Pump and Dump in DeFi via Graph and Contrastive Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TIFS.2025.3594873 |
| Publisher version: | https://doi.org/10.1109/tifs.2025.3594873 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10212382 |
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