Early Detection of Malicious Crypto Addresses With Asset Path Tracing and Selection

Abstract

In response to the burgeoning cryptocurrency sector and its associated financial risks, there is a growing focus on detecting fraudulent activities and malicious addresses. Traditional studies are limited by their reliance on comprehensive historical data and address-wise manipulation, which are not available for early malice detection and fail to identify addresses controlled by the same fraudulent entity. We thus introduce Evolve Path Tracer, a novel solution designed for early malice detection in cryptocurrency. This system innovatively incorporates Asset Transfer Paths and corresponding path graphs in an evolve model, which effectively characterize rapidly evolving transaction patterns. First, for the target address, the Clustering-based Path Selector weight each Asset Transfer Path by finding sibling addresses along the Asset Transfer Paths. Evolve Path Encoder LSTM and Evolve Path Graph GCN then encode the asset transfer path and path graph within a dynamic structure. Additionally, our Hierarchical Survival Predictor efficiently scales to predict the address labels, demonstrating high scalability and efficiency. We rigorously tested Evolve Path Tracer on three real-world datasets of malicious addresses, where it consistently outperformed existing state-of-the-art methods. Our extensive scalability tests further confirmed the model's robust adaptability in dynamic prediction environments, highlighting its potential as a significant tool in the realm of cryptocurrency security.