# Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions

arXiv: https://arxiv.org/abs/2504.17953 · HTML: https://mlsanity.com/paper/2504.17953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [Fence GAN: Towards Better Anomaly Detection](https://mlsanity.com/paper/1904.01209/) (2019)
- [A Survey on Ethereum Systems Security: Vulnerabilities, Attacks and Defenses](https://mlsanity.com/paper/1908.04507/) (2019)
- [TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection](https://mlsanity.com/paper/2204.13442/) (2022)
- [Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks](https://mlsanity.com/paper/2204.09829/) (2022)
- [Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)](https://mlsanity.com/paper/2206.08202/) (2022)
- [TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum](https://mlsanity.com/paper/2208.12938/) (2022)
- [Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency](https://mlsanity.com/paper/2301.05412/) (2023)
- [Countering Selfish Mining in Blockchains](https://mlsanity.com/paper/1811.09943/) (2018)
- [Towards Blockchain-Driven, Secure and Transparent Audit Logs](https://mlsanity.com/paper/1811.09944/) (2018)
- [Explainable Ponzi Schemes Detection on Ethereum](https://mlsanity.com/paper/2301.04872/) (2023)
- [Contra-*: Mechanisms for Countering Spam Attacks on Blockchain's Memory Pools](https://mlsanity.com/paper/2005.04842/) (2020)
- [From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency](https://mlsanity.com/paper/2309.15133/) (2023)
- [Dissecting the Infrastructure Used in Web-based Cryptojacking: A Measurement Perspective](https://mlsanity.com/paper/2408.03426/) (2024)
- [Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis](https://mlsanity.com/paper/2408.03441/) (2024)

## Abstract (arXiv)

Phishing detection on Ethereum has increasingly leveraged advanced machine learning techniques to identify fraudulent transactions. However, limited attention has been given to understanding the effectiveness of feature selection strategies and the role of graph-based models in enhancing detection accuracy. In this paper, we systematically examine these issues by analyzing and contrasting explicit transactional features and implicit graph-based features, both experimentally and analytically. We explore how different feature sets impact the performance of phishing detection models, particularly in the context of Ethereum's transactional network. Additionally, we address key challenges such as class imbalance and dataset composition and their influence on the robustness and precision of detection methods. Our findings demonstrate the advantages and limitations of each feature type, while also providing a clearer understanding of how feature affect model resilience and generalization in adversarial environments.