-------------------------------------------------------------------------------- TITLE PAGE (upload as the single Cover Letter file) -------------------------------------------------------------------------------- # Title Page **Title:** Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques (A Structured Narrative Review) **Article type:** Review article. Research design: structured narrative review (the article reports a methodological synthesis of the blockchain anomaly- and fraud-detection literature for a finance audience; it is not a primary empirical study). **Authors and affiliations** (listed in the same order as in the submission system): 1. **Prof. Dr. Joerg Osterrieder** (Corresponding author) Associate Professor of Finance and Artificial Intelligence, University of Twente, Department of High-Tech Business and Entrepreneurship, Enschede, Netherlands; Action Chair, EU COST Action CA19130 (Fintech and Artificial Intelligence in Finance); Coordinator, Marie Sklodowska-Curie Industrial Doctoral Network on Digital Finance. Email: joerg.osterrieder@utwente.nl 2. **Prof. Dr. Stephen Chan** Associate Professor of Statistics, American University of Sharjah, Department of Mathematics and Statistics, Sharjah, United Arab Emirates. Email: schan@aus.edu 3. **Prof. Dr. Jeffrey Chu** Assistant Professor of Statistics, Renmin University of China, School of Statistics, Beijing, China. Email: jeffrey.jchu@ruc.edu.cn 4. **Dr. Yuanyuan Zhang** Research Associate, American University of Sharjah, Department of Mathematics and Statistics, Sharjah, United Arab Emirates. Email: yzhang@aus.edu 5. **Prof. Dr. Codruta Mare** Professor of Statistics, Babes-Bolyai University, Faculty of Economics and Business Administration (Department of Statistics-Forecasts-Mathematics), and the Interdisciplinary Centre for Data Science, Cluj-Napoca, Romania. Email: codruta.mare@econ.ubbcluj.ro **Corresponding author:** Prof. Dr. Joerg Osterrieder, University of Twente, Department of High-Tech Business and Entrepreneurship, Enschede, Netherlands. Email: joerg.osterrieder@utwente.nl. > NOTE TO AUTHORS (remove before upload): The author order and the corresponding-author designation above must match the order entered in the Editorial Manager submission system. Please confirm Dr. Yuanyuan Zhang's exact role title and institutional email, and confirm the corresponding-author email, before submitting. ORCID iDs are entered directly in Editorial Manager. -------------------------------------------------------------------------------- # Authors' Information (Short Bios) > NOTE TO AUTHORS (remove before upload): The biographies below are concise drafts built from each author's current role and institution. Please review and expand each one with your own qualifications, current positions, scientific interests, awards, and society memberships before submission. **Joerg Osterrieder** is Associate Professor of Finance and Artificial Intelligence at the University of Twente (Department of High-Tech Business and Entrepreneurship), Netherlands. He chairs the EU COST Action CA19130 on Fintech and Artificial Intelligence in Finance and coordinates the Marie Sklodowska-Curie Industrial Doctoral Network on Digital Finance. His research interests centre on digital finance, machine learning in finance, and the governance of financial technology. **Stephen Chan** is Associate Professor of Statistics at the American University of Sharjah (Department of Mathematics and Statistics), United Arab Emirates. His research interests include statistical modelling, the analysis of cryptocurrency and financial-market data, and applied probability. **Jeffrey Chu** is Assistant Professor of Statistics at the School of Statistics, Renmin University of China, Beijing. His research interests include statistical methods for cryptocurrencies and financial data and computational statistics. **Yuanyuan Zhang** is a Research Associate at the American University of Sharjah (Department of Mathematics and Statistics), United Arab Emirates. Her research interests include statistical modelling and the analysis of financial and blockchain data. **Codruta Mare** is Professor of Statistics at Babes-Bolyai University, Faculty of Economics and Business Administration (Department of Statistics-Forecasts-Mathematics), and a member of the Interdisciplinary Centre for Data Science, Cluj-Napoca, Romania. Her research interests include applied statistics and econometrics, data science, and quantitative methods in economics and finance. -------------------------------------------------------------------------------- # Cover Letter Dear Prof. Kou and the Editorial Board of *Financial Innovation*, We are pleased to submit our manuscript, "Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques," for consideration as a review article in *Financial Innovation*. Blockchain networks now underpin multi-trillion-dollar cryptoasset markets and a fast-growing set of decentralised-finance applications, which makes the detection of anomalies and frauds in these networks a first-order concern for financial integrity, investor protection, and systemic stability. Our review synthesises the detection literature for a finance audience rather than a computer-science one: it is anchored throughout in the financial-market consequences of security failures, that is, market integrity, regulatory compliance, user protection, and systemic risk, rather than in the underlying cryptographic machinery. The manuscript makes four contributions. First, it develops a dimensional taxonomy of blockchain anomalies and frauds, indexed by blockchain layer, anomaly or fraud class, and detection method. Second, it provides a comparative matrix of detection techniques organised by data type and supervision regime, designed as a practical lookup tool for selecting a technique for a given use case. Third, it positions the synthesis explicitly against the prior general-purpose anomaly-detection surveys, clarifying what is specific to the blockchain and finance setting. Fourth, it sets out a research agenda for the 2022-2026 frontier, covering cross-chain bridge monitoring, DeFi flash-loan attack detection, and explainable-AI models suited to regulatory compliance. The review is anchored in documented incidents, the 2016 Ethereum denial-of-service attacks, the 2014 Mt. Gox collapse, and the 2021 Poly Network cross-chain exploit, which we use to trace recurring failure modes at the contract, exchange, and cross-chain layers. Methodologically the paper is a structured narrative review; its scope, the databases queried, the search terms, and the inclusion and exclusion criteria are reported in full in the manuscript, and every reference was verified against authoritative bibliographic metadata. We believe the review fits squarely within *Financial Innovation*'s readership of fintech researchers and practitioners, and to our knowledge no existing review organises the detection literature along these axes for a finance audience. The manuscript is original, is not under consideration elsewhere, and all authors have approved the submission. Thank you for considering our work. We look forward to the editorial and reviewer feedback. Sincerely, Prof. Dr. Joerg Osterrieder (Corresponding author) University of Twente, Netherlands Email: joerg.osterrieder@utwente.nl On behalf of S. Chan, J. Chu, Y. Zhang, and C. Mare. -------------------------------------------------------------------------------- # Declarations ## Ethical Approval and Consent to participate Not applicable. This work is a structured narrative review of previously published literature and involved no human participants, their data, or animals. ## Consent for publication Not applicable. The manuscript contains no data, images, or details relating to any individual person. ## Availability of supporting data This is a structured narrative review; no primary datasets were generated or analysed during this study. The secondary sources supporting the case studies in Chapter 5 are all cited in the reference list and are publicly available. ## Competing interests The authors declare that they have no competing interests. ## Funding This work has been supported by COST Action CA19130 and COST Action CA21163 (COST, European Cooperation in Science and Technology); the Marie Sklodowska-Curie Actions under the European Union's Horizon Europe research and innovation programme for the Industrial Doctoral Network on Digital Finance (Project No. 101119635); and the Swiss National Science Foundation (IZCNZ0-174853, IZSEZ0-211195, IZCOZ0-213370). It has further been supported by American University of Sharjah Faculty Research Grant 2023 (FRG23-C); European Union Horizon 2020 grant No. 825215 (FIN-TECH); and Babes-Bolyai University (PFE-550-UBB). This work was also supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI, project number PN-IV-P2-2.1-TE-2023-1317, within PNCDI IV. ## Authors' contributions The authors contributed according to the CRediT taxonomy as follows. - **Joerg Osterrieder (JO):** Conceptualization; Methodology; Writing (original draft); Writing (review and editing); Supervision; Funding acquisition; Project administration. Corresponding author. - **Stephen Chan (SC):** Conceptualization; Methodology; Writing (original draft); Writing (review and editing). - **Jeffrey Chu (JC):** Conceptualization; Methodology; Writing (original draft); Writing (review and editing). - **Yuanyuan Zhang (YZ):** Conceptualization; Methodology; Writing (original draft); Writing (review and editing). - **Codruta Mare (CM):** Conceptualization; Methodology; Writing (review and editing); Funding acquisition. All authors read and approved the final manuscript. ## Acknowledgements We acknowledge the members of the European Industrial Doctoral Network on Digital Finance, and the COST Action CA19130 Management Committee and Working Group members. Any errors are the authors' own. ## Authors' information (Optional) Short biographies of all contributing authors are provided in the "Authors' Information (Short Bios)" section above. ## Use of AI and AI-assisted technologies In compliance with Springer Nature's 2023 policy on AI tools in scholarly publishing: the authors used Claude (Anthropic) for language polishing, formulaic-phrasing removal, and verification of AI-generated text patterns; and used the OpenAlex REST application programming interface for post-hoc verification of citation metadata. AI tools did not contribute authorial content, methodological design, reference selection, or case-study interpretation beyond language polishing. Every AI-assisted edit was reviewed by at least one author.