Anomaly detection in cryptocurrency networks and beyond

Abstract

Cryptocurrency networks that provide a new way of securing financial transactions have gained a surge of interest in recent years. However, recent studies reveal that blockchain networks are rampant with frauds and are prone to several privacy and security issues. The public availability of cryptocurrency transaction records provides an unprecedented opportunity for researchers to analyze cryptocurrency transactions. In particular, anomaly detection techniques are promising avenues for fighting illicit activities such as money laundering, terrorist financing, drug trafficking, scams, frauds, and many more.In this dissertation, we address the challenges of detecting anomalous entities on cryptocurrency networks. Firstly, we introduce effective techniques for generating features for network entities that are directly devised from raw data and highlight the utility of those features in detecting illicit accounts on the Ethereum network. Next, we enrich the proposed method by expanding the feature set through the incorporation of graph-based features that embed the relational information of networks. This also enables us to generalize our methods to instances of cryptocurrency networks with different architectural models. Based on the success of our method in anomaly detection in cryptocurrency networks, we further generalize our model to encompass a generic temporal weighted multidigraph and show the state-of-the-art results for anomaly detection in other common domains including rating and social networks. In doing so, we also investigate the challenges of employing node classification techniques for anomaly detection, which is a common practice. Here, we discuss the importance of performance metrics and evaluation settings when interpreting the efficiency of different methods and tasks, which is often overlooked by the community. Finally, we shift our focus to examining the inherent challenges of learning on dynamic networks, which is an important emerging research field with applications in drug discovery, computational finance, social networks, etc. Here, we propose solutions for providing a more robust evaluation setup for dynamic graph learning methods. The key contributions of this dissertation are twofold: First, we describe efficient techniques for detecting anomalies on cryptocurrency networks and generalize them to other real-world complex networks. Second, we focus on the temporal aspect of these networks and investigate how the dynamism of networks affects the downstream tasks and evaluation settings