Anomaly and Fraud Detection in Blockchain Networks
Swiss National Science Foundation | CHF 300K | 2019–2022 —
Overview
SNSF-funded international research collaboration developing methods for detecting anomalies and fraud in blockchain-based and cryptocurrency networks.
Grant Number: IZSEZ0_211195
Role
Principal Investigator - Led international research team across multiple institutions.
Research Team
- Prof. Stephen Chan (American University of Sharjah)
- Dr. Yuanyuan Zhang (University of Manchester)
- Prof. Jeffrey Chu (Renmin University)
- Prof. Codruta Mare (Babes-Bolyai University)
- Prof. Branka Hadji Misheva (Bern Business School)
Research Focus
Despite the advantages of blockchain networks — transparency, immutability, decentralisation — they remain susceptible to:
- Transaction anomalies
- Smart contract vulnerabilities
- Fraud schemes
- Network attacks
Detection Methods
| Category | Techniques |
|---|---|
| Statistical Methods | Outlier detection, change point analysis |
| Machine Learning | Supervised classification, unsupervised clustering |
| Game Theory | Incentive mechanism design |
| Digital Forensics | Transaction tracing, wallet clustering |
| Reputation Systems | Trust scoring, behavioral analysis |
Key Publications
- arXiv (2024): Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques
- SSRN: A Primer on Anomaly and Fraud Detection in Blockchain Networks
Project Completion
Completed in 2024. The project delivered methods and techniques that address digital-finance security challenges and highlight the need for updated regulatory approaches to blockchain oversight.
Links
- SNSF Grant Database
- University of Applied Sciences of the Grisons — Research Project Partner