Early Detection of Malicious Crypto Addresses With Asset Path Tracing and Selection
Ling Cheng, Feida Zhu, Qian Shao, Jiashu Pu, Fengzhu Zeng · IEEE Transactions on Knowledge and Data Engineering · 2024
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 introduceEvolve 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, theClustering-based Path Selectorweight each Asset Transfer Path by finding sibling addresses along the Asset Transfer Paths.Evolve Path Encoder LSTMandEvolve Path Graph GCNthen encode the asset transfer path and path graph within a dynamic structure. Additionally, ourHierarchical Survival Predictorefficiently scales to predict the address labels, demonstrating high scalability and efficiency. We rigorously testedEvolve Path Traceron 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.