On the Use of Heterogeneous Graph Neural Networks for Detecting Malicious Activities: a Case Study with Cryptocurrencies

Abstract

This paper presents a study on the application of Heterogeneous Graph Neural Networks (HGNNs) for enhancing the security of complex social systems by identifying illicit and malicious behaviors. We focus on digital asset tokenization, a key component in the construction of many innovative social services, with the aim of classifying token exchanges and identifying illicit activities. Utilizing the Elliptic++ dataset, we demonstrate the efficacy of HGNNs in identifying illicit activities in token-based exchanging applications. In particular, we evaluate four different HGNN architectures, i.e. Heterogeneous GAT, Heterogeneous SAGE, HGT (Heterogeneous Graph Transformer), and HAN (Heterogeneous Attention Network). Our results underscore the importance of characterizing and describing interactions in these complex systems, both for studying the system dynamics and for activating mechanisms to cope with cybersecurity issues, like misuses and usurpation of resources in social systems.