Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques

TL;DR

This paper provides an in-depth review of anomalies and frauds in blockchain networks, analyzing detection strategies like machine learning and game theory, supported by case studies and future research directions.

Contribution

It offers a comprehensive overview of detection techniques and practical insights, advancing understanding of blockchain security challenges and solutions.

Findings

Detection methods include statistical, machine learning, and game-theoretic approaches.

Case studies demonstrate practical application of detection strategies.

Highlights emerging trends and future challenges in blockchain security.

Abstract

Blockchain technology, a foundational distributed ledger system, enables secure and transparent multi-party transactions. Despite its advantages, blockchain networks are susceptible to anomalies and frauds, posing significant risks to their integrity and security. This paper offers a detailed examination of blockchain's key definitions and properties, alongside a thorough analysis of the various anomalies and frauds that undermine these networks. It describes an array of detection and prevention strategies, encompassing statistical and machine learning methods, game-theoretic solutions, digital forensics, reputation-based systems, and comprehensive risk assessment techniques. Through case studies, we explore practical applications of anomaly and fraud detection in blockchain networks, extracting valuable insights and implications for both current practice and future research. Moreover, we spotlight emerging trends and challenges within the field, proposing directions for future investigation and technological development. Aimed at both practitioners and researchers, this paper seeks to provide a technical, in-depth overview of anomaly and fraud detection within blockchain networks, marking a significant step forward in the search for enhanced network security and reliability.