Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions
This work addresses phishing detection for Ethereum users, but it appears incremental as it focuses on comparing existing feature types rather than introducing a novel method.
The paper tackled phishing detection in Ethereum transactions by systematically comparing explicit transactional features and implicit graph-based features, finding that each feature type has distinct advantages and limitations for model performance.
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.