Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908337246830592 |
|---|---|
| author | Alghuried, Ahod Alghamdi, Abdulaziz Alkinoon, Ali Choi, Soohyeon Mohaisen, Manar Mohaisen, David |
| author_facet | Alghuried, Ahod Alghamdi, Abdulaziz Alkinoon, Ali Choi, Soohyeon Mohaisen, Manar Mohaisen, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions Alghuried, Ahod Alghamdi, Abdulaziz Alkinoon, Ali Choi, Soohyeon Mohaisen, Manar Mohaisen, David Cryptography and Security Machine Learning 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. |
| title | Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2504.17953 |