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\title{Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques}
\author{Joerg Osterrieder\thanks{University of Twente, NL. \textbf{Corresponding author: joerg.osterrieder@utwente.nl}} \and
 Stephen Chan\thanks{American University of Sharjah, UAE.} \and
 Jeffrey Chu\thanks{Renmin University of China.} \and
 Yuanyuan Zhang\thanks{American University of Sharjah, UAE.} \and
 Codruta Mare\thanks{Babe\c{s}-Bolyai University, Romania.}}
\date{\today}

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\begin{abstract}
\noindent Blockchain networks underpin multi-trillion-dollar cryptoasset markets and an expanding set of decentralised-finance applications, making their security posture a first-order concern for financial integrity, investor protection, and systemic risk: anomalies and frauds in these networks affect price formation, market microstructure, and the reliability of the financial infrastructure they support. This review synthesises 55 references (31 from the original reference set after removing 4 URL-only opinion-piece entries, 24 added for reference currency, case-study verification, finance-framing, and a game-theory-in-blockchain survey) into a dimensional taxonomy indexed by blockchain layer, anomaly or fraud class, and detection method, and organises the evidence into four active detection families (statistical, machine-learning including deep learning, game-theoretic, and digital-forensic) that differ along the data, transparency, and scalability dimensions. Case-study evidence from Ethereum's 2016 denial-of-service incidents, the 2014 Mt.~Gox exchange collapse, and the 2021 Poly Network cross-chain exploit illustrates recurring failure modes at the contract, exchange, and cross-chain layers respectively. The review contributes a blockchain-layer axis for indexing existing detection techniques (the detection-method decomposition itself reproduces prior conventions in \citet{chandola2009anomaly}, \citet{akoglu2014graph}, and \citet{ahmed2015a}), a comparative matrix of detection techniques by data type and supervision regime, an explicit positioning against those three prior general-purpose surveys, and a research agenda for the 2022--2026 frontier covering cross-chain bridge exploits, DeFi flash-loan attacks, and explainable-AI approaches to detection.
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\noindent\textbf{Keywords:} Blockchain Security, Anomaly Detection, Fraud Detection, Distributed Ledger Technology, Machine Learning, Game Theory, Digital Forensics, Financial Innovation

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\noindent\textbf{JEL Codes:} G23, G10, C63, C45, G29

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