Summary of Enhancing Ethereum Fraud Detection Via Generative and Contrastive Self-supervision, by Chenxiang Jin et al.
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision
by Chenxiang Jin, Jiajun Zhou, Chenxuan Xie, Shanqing Yu, Qi Xuan, Xiaoniu Yang
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to fraud detection on Ethereum, a blockchain ecosystem plagued by fraudulent activities. The authors identify multiple imbalances in the transaction environment that hinder data mining-based fraud detection research. They introduce the concept of meta-interactions to refine interaction behaviors and present a dual self-supervision enhanced framework called Meta-IFD. This framework uses generative and contrastive self-supervision mechanisms to characterize account behavior patterns, mine potential fraud risks, and alleviate interaction distribution imbalances. Experimental results on real Ethereum datasets demonstrate the effectiveness and superiority of Meta-IFD in detecting common fraud behaviors such as Ponzi schemes and phishing scams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ethereum is a popular blockchain that has been hit by many fraudulent activities. To help prevent these frauds, researchers have developed new ways to detect them. One problem they face is that the data on Ethereum transactions is not equally distributed, making it harder to find patterns. The authors of this paper introduce a new approach called Meta-IFD that uses machine learning to detect fraudulent activities on Ethereum. It works by refining the way we understand how accounts interact with each other and then using that understanding to find potential fraud risks. |
Keywords
» Artificial intelligence » Machine learning
