Verification of Elemental Technologies for Anomaly Detection in Crypto Asset Transactions

Realizing a cyber-physical economy requires dealing with the problems of the digital society that have arisen with the development of information technology. This study systematizes the mathematical basis for detecting anomalies for a dynamic graph, a network representation of relationships among nodes of crypto asset transactions and changes as time passes, based on graph theory, topology, and high-dimensional statistical analysis, to answer the three research questions: (1) Are there leading indicators of transactions that precede prices? (2) Is there a correlation between the velocity of circulation and prices? (3) Is there a herding phenomenon in the transaction network? Here, we define “anomaly†as large price fluctuations that affect transactions. The multiple methods above are applied to dynamic graphs during higher priced periods of crypto asset transactions to estimate individual anomaly indicators. We verify the effectiveness of the various anomaly detection methods by answering the three research questions for a major crypto asset. Finally, we propose a concept for an anomaly detection AI that estimates a comprehensive anomaly indicator by inputting various features from individual analysis methods.