Anomaly and Fraud Detection in Blockchain Networks
A collaborative research initiative between American University of Sharjah, UAE and Bern Business School, Switzerland, developing advanced detection methods for securing blockchain-based systems.
Learn MoreResearch Overview
Understanding and preventing anomalies and fraud in blockchain-based networks
Background
Blockchain networks are increasingly being implemented into healthcare, supply chain, and retail systems through smart contracts and smart devices. While this technology brings benefits, the immutability property means that fraudulent transactions cannot be easily reversed, making early detection critical.
Rationale
Blockchains can be attacked via a deluge of requests or transactions within a short time span, resulting in loss of connectivity for users and businesses. Rapid detection of anomalies is critical to prevent damage or correct issues as soon as possible.
Objectives
This project studies anomaly and fraud detection from the perspective of blockchain-based networks. Detection is more complex due to unique properties such as decentralisation, global reach, and anonymity which make them different from traditional networks.
Specific Aims
To further the understanding of the sources and behaviours of anomalies and fraud in blockchain-based networks, and develop new improved methods for both static and dynamic anomaly detection that can be used alongside blockchain-based systems for real-time fraud detection.
Methods
Developing static anomaly detection via hybrid approaches and dynamic anomaly detection using extreme value theory. Our methods combine machine learning, game theory, digital forensics, and reputation-based systems.
Expected Results
This research contributes to improving the security of blockchain-based networks by providing more accurate and efficient methods for detecting anomalies and fraud, reducing the impact of losses resulting from these anomalies.
Impact for the Field
The project is particularly beneficial alongside real-world blockchain-based networks to allow for fast detection of anomalous or fraudulent data, preventing damage or allowing for damage to be corrected as soon as possible. For cryptocurrency networks, this reduces the impact of market manipulation, fraud, and more widely on global financial markets, currencies, and trade. The project is of interest to a broad range of stakeholders including academics, financial institutions, policymakers, regulators, and cybercrime agencies.
Our Team
International collaboration between leading researchers
Joerg Osterrieder
Principal Investigator
Bern Business School, Switzerland
University of Twente, Netherlands
Yiting Liu
Team Member
Bern Business School, Switzerland
University of Twente, Netherlands
Lennart John Baals
Team Member
Bern Business School, Switzerland
University of Twente, Netherlands
Research Output
Key indicators showing the impact of this project
Publications
Our research contributions to the field
GARCH Modelling of Cryptocurrencies
Journal of Risk and Financial Management, 10(4), 17
Citations: 340
The first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, with conclusions on best fitting models, forecasts, and acceptability of value at risk estimates.
A Statistical Analysis of Cryptocurrencies
Journal of Risk and Financial Management, 10(2), 12
Citations: 213
Statistical analysis of the largest cryptocurrencies by market capitalization, characterizing exchange rates versus the US Dollar by fitting parametric distributions. Shows returns are non-normal with generalized hyperbolic distribution giving best fit for Bitcoin and Litecoin.
Stylized Facts of Metaverse Non-Fungible Tokens
Physica A: Statistical Mechanics and its Applications, 653, 130103
Citations: 6 (Top 1% citation impact)
Comprehensive examination of NFTs within the Metaverse with comparative analysis using Metaverse indices from Bloomberg and Yield Guild Games data.
Lead Behaviour in Bitcoin Markets
Risks, 8(1), 4
Understanding the dynamics of Bitcoin blockchain trading volumes using an extended Vector Autoregressive model. Shows transactions dominated by network participants in Europe and the United States, consistent with market interactions in developed economies.
Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques
arXiv preprint
Detailed examination of blockchain's key definitions and properties, alongside thorough analysis of various anomalies and fraud patterns in blockchain networks.
Metaverse Non-Fungible Tokens
SSRN Working Paper
A comprehensive examination of Non-Fungible Tokens (NFTs) within the Metaverse, reviewing the Metaverse's evolution, the emergence of NFTs, and their transformative benefits.
DOI: 10.2139/ssrn.4733153Leveraging Network Topology for Credit Risk Assessment in P2P Lending
Expert Systems with Applications, 252(B), 124100
A comparative study using machine learning and network analysis for P2P credit default prediction. Graph-based models improve prediction accuracy using network centrality features from Bondora platform data.
Network Centrality and Credit Risk: A Comprehensive Analysis of P2P Lending Dynamics
Finance Research Letters, 63(C), 105286
Analysis of credit risk in P2P lending using network topological features. Degree centrality enhances predictive power in default likelihood modeling, demonstrating the significance of borrower network position on default risk.
Datasets
Open data resources from our research
Cryptocurrency Market Data
Historical price, volume, and market capitalization data for major cryptocurrencies used in our statistical analysis and GARCH modelling research.
View on GitHubBlockchain Network Data
Network graph data from blockchain transactions used for anomaly detection and fraud pattern analysis research.
View on GitHubAcademic Events
The team has received invitations to numerous international conferences as keynote speakers and session chairs
Seminar-AUS 2024
American University of Sharjah, UAE
Dr. Osterrieder was invited as keynote speaker at the inaugural research conference on Mathematics and Related Areas. Talk titled "Data Science in Finance - Applications" covering mathematical concepts and their applications for research initiatives in Digital Finance.
AUS-ICMS 2025
American University of Sharjah, UAE
AUS Fourth International Conference on Mathematics and Statistics. Joerg Osterrieder serves on the International Advisory Board. Special Session on Statistics and Data Science for Digital Finance with talks by Osterrieder, Chan, Chu, and Zhang.
Knowledge Transfer
Bridging research and practice through education and industry engagement
PhD Curriculum Development
2022-2024
Development of a new PhD curriculum for Digital Finance at the University of Twente and BFH, integrating blockchain technology, AI, and financial innovation.
Industry Workshops
2023-2024
Series of workshops with financial institutions on blockchain anomaly detection methods and practical implementation of fraud detection systems.
Collaborations
International research partnerships and networks
American University of Sharjah
Department of Mathematics, UAE
Prof. Dr. Stephen Chan
- In-depth constructive exchanges on approaches, methods, or results
- Publications
- Exchange of personnel
University of Manchester
Department of Mathematics, UK
Dr. Yuanyuan Zhang
- In-depth constructive exchanges on approaches, methods, or results
- Publications
Renmin University of China
Department of Mathematics, China
Prof. Dr. Jeffrey Chu
- In-depth constructive exchanges on approaches, methods, or results
- Publications
Babes-Bolyai University
Department of Statistics, Romania
Prof. Dr. Codruta Mare
- In-depth constructive exchanges on approaches, methods, or results
- Publications
- Exchange of personnel
COST Action CA19130
Fintech and Artificial Intelligence in Finance
Action Chair: Joerg Osterrieder
- European research network
- Publications and personnel exchange
MSCA Industrial Doctoral Network
Digital Finance - Reaching New Frontiers
Coordinator: Joerg Osterrieder
- Horizon Europe funded
- 4.5 Million EUR
Funding & Third-Party Funds
The team has acquired several large national and international research funds
Follow-up Projects
Ongoing and future research initiatives building on this project
MSCA Industrial Doctoral Network on Digital Finance
2024 - 2027
Doctoral training program combining academic research with industry experience in blockchain technology, cryptocurrencies, and financial data science.
COST Action CA19130
Fintech and Artificial Intelligence in Finance, 2024
European research network for collaboration on emerging trends in fintech and AI.
International Workshop Series
Starting 2025
Annual international workshops on blockchain and digital finance, rotating among partner institutions.
NSF-SNF Research Application
Planned 2025-2026
Joint application to National Science Foundation (US) and Swiss National Science Foundation for blockchain security and digital finance research.
Partner Institutions
A collaborative effort between leading research institutions
Get in Touch
Interested in our research? Contact us for collaboration opportunities.
Funded by the European Union. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or Horizon Europe: Marie Sklodowska-Curie Actions. This project has received funding from the Horizon Europe research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 101119635.