A Primer on Anomaly and Fraud Detection in Blockchain Networks

Abstract

Blockchain technology is a distributed ledger system that allows multiple parties to record and verify transactions in a secure and transparent manner. However, blockchain networks are vulnerable to anomalies and frauds that can have serious consequences for the integrity and security of these networks. In this primer, we provide an overview of the definition and properties of blockchain technology, and discuss the types and examples of anomalies and frauds that occur in these networks. We also examine the techniques and technologies that are used to detect and prevent these attacks, including statistical approaches, machine learning approaches, game-theoretic approaches, digital forensics, reputation-based systems, and risk assessment systems. We present case studies of anomaly and fraud detection in real-world blockchain networks, and discuss the lessons learned from these cases and their implications for future research and practice. We also identify emerging trends and challenges in the field, and discuss potential future research directions and technologies. This primer is intended as a resource for practitioners and researchers in the field of anomaly and fraud detection in blockchain networks, and aims to provide a technical and comprehensive overview of this growing and important field.

Keywords: Blockchain, Anomaly detection, Fraud detection, Machine learning, Game theory

JEL Classification: G40