Uncovering Fraud in NFT Ecosystems

Data Engineering & Digital Transformation

Technologies for a secure Society and Cybersecurity

05/05/2026

This research, conducted by investigators from theInformation Processing and Telecommunications Center (IPTC), research center of the Universidad Politécnica de Madrid (UPM) explores how suspicious and potentially fraudulent activities can be detected within the NFT ecosystem. As NFT markets grow rapidly, they also face increasing risks such as price manipulation, wash trading, and coordinated behavior among users.

The study introduces an innovative approach based on temporal graph analysis, which examines not only the relationships between participants but also how these interactions evolve over time. Instead of analyzing isolated transactions, the researchers model the NFT ecosystem as a dynamic network where users and assets are connected through time-dependent interactions.

By leveraging this method, the research is able to identify unusual patterns of behavior that may indicate coordinated or deceptive actions. This includes detecting clusters of accounts that interact in suspicious ways, as well as temporal patterns that are difficult to observe with traditional static analysis techniques.

The results demonstrate that incorporating the time dimension significantly improves the ability to uncover hidden structures and anomalies in NFT trading activity.

🔍 Potential Applications

Fraud and scam detection in NFT marketplaces
Monitoring and regulation of digital asset ecosystems
Enhancing trust and transparency in blockchain platforms
Supporting cybersecurity tools focused on financial technologies

✅ Conclusion

Overall, this work highlights the importance of advanced data analysis techniques in addressing emerging challenges in digital markets. By combining network analysis with temporal dynamics, the study provides a powerful tool for identifying hidden risks and improving the security and reliability of NFT ecosystems.

Bibliographic reference:

Sliti, W., Cuadrado, F., Campos-Hernáez, L., & Dueñas, J.C. Detecting Suspicious Activity in the NFT Ecosystem Using Temporal Graph Analysis in IEEE Transactions on Network Science and Engineering, 13, pp. 8684-8701. https://doi.org/10.1109/TNSE.2026.3684082

Authors:

Wassin Sliti: GS / ORCID / LinkedIn

Félix Cuadrado: GS / ORCID / LinkedIn

Juan Carlos Dueñas: GS / ORCID / LinkedIn

LinkedIn: https://www.linkedin.com/company/iptc-upm/

For more information: www.iptc.upm.es