# Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain

arXiv: https://arxiv.org/abs/2412.12370 · HTML: https://mlsanity.com/paper/2412.12370/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [PageRank Bandits for Link Prediction](https://mlsanity.com/paper/2411.01410/) (2024)
- [The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts](https://mlsanity.com/paper/1902.06976/) (2019)
- [Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction](https://mlsanity.com/paper/2410.20253/) (2024)
- [Influence Pathway Discovery on Social Media](https://mlsanity.com/paper/2309.16071/) (2023)
- [Tracking Counterfeit Cryptocurrency End-to-end](https://mlsanity.com/paper/2011.02673/) (2020)
- [Don't Fish in Troubled Waters! Characterizing Coronavirus-themed Cryptocurrency Scams](https://mlsanity.com/paper/2007.13639/) (2020)

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

- [Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing](https://mlsanity.com/paper/2505.11743/) (2025)
- [Data Augmentation Through Random Style Replacement](https://mlsanity.com/paper/2504.10563/) (2025)
- [A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond](https://mlsanity.com/paper/2508.11957/) (2025)
- [Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation](https://mlsanity.com/paper/2505.17391/) (2025)
- [DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA](https://mlsanity.com/paper/2506.00671/) (2025)

## Abstract (arXiv)

As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts, that is, to represent Ethereum transaction data as graphs, and then use advanced ML technology to obtain reliable and accurate results. Taking into account the sample imbalance, we treated with SMOTE-ENN and tested several models, in which MLP performed better than GCN, but the exact effect depends on its field trials. Our research opens up more possibilities for trust and security in the Ethereum ecosystem.