  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.
