Visual Analytics for Phishing Scam Identification in Blockchain Transactions with Multiple Model Comparison.

The phishing scam is a major kind of fraudulence in blockchain. And it has become an urgent issue to discern and prevent the fraudulent behaviors. However, the large-scale and dynamic nature of transaction network imposes great challenges on the identification and analysis. While there have been many sophisticated machine learning approaches providing predictive capability in terms of detecting such cases, they usually offer little insight into the essence of those behaviors and the occasion when phishing scam activities happen. Motivated by these shortcomings and bottlenecks, this paper proposes a suite of visual analytical methods for interpretable and explorable fraudulence identification in large-scale blockchain transaction networks, incorporating an anomaly detection model based on multiple feature extraction manners. In this paper, we adopt two types of graph embedding methods and variable derivation to generate features from transaction data. Then we use machine learning classification approaches to fit the three sets of features. Evaluations show that all kinds of features perform well in classification. Besides, we design an interactive visualization system displaying the transaction networks and classification models, which allows users better explore the data and understand the models. Furthermore, we demonstrate two cases through the visualization system to unearth fraudulent patterns and interpret classification results. Finally, we close with discussions for further improvements of our models and system.