Research on Blockchain Security Detection Based on Machine Learning

Yukang Fang

doi:10.54097/993mxc09

Research on Blockchain Security Detection Based on Machine Learning

Yukang Fang

Frontiers in Computing and Intelligent Systems, (2026), Vol.18, No.2, pp.12-18

Published: October 29, 2026

DOI: 10.54097/993mxc09

PDF: Download Full Text PDF

Abstract

With the rapid development of blockchain technology, its decentralized, tamper-resistant and traceable characteristics have been widely adopted in fields such as finance, supply chain management and the Internet of Things. However, the openness of blockchain ecosystems also creates opportunities for criminals, posing serious threats to the stability of blockchain networks and the security of user assets. To address the difficulties in identifying on-chain malicious transactions, the low audit efficiency of smart contract vulnerabilities, and the complexity and poor reproducibility of existing models in traditional blockchain scenarios, this paper proposes a lightweight machine learning-based detection scheme. The Elliptic transaction dataset and a smart contract vulnerability dataset constructed from the Slither official test suite and SWC (Smart Contract Weakness Classification) standard vulnerability cases are adopted, and random forest, XGBoost, LSTM and CNN are selected to build detection models. Multi-model comparative experiments are conducted with accuracy, precision, recall, F1-score and AUC as evaluation metrics. Experimental results show that the proposed scheme can effectively detect malicious transactions and contract vulnerabilities, providing a reference for intelligent blockchain security protection.

Keywords: Blockchain Security, Malicious Transaction Detection, Smart Contract Vulnerability, Machine Learning, Lightweight Detection

APA Citation: Yukang Fang (2026). Research on Blockchain Security Detection Based on Machine Learning. Frontiers in Computing and Intelligent Systems, 18(2), 12-18. https://doi.org/10.54097/993mxc09

References

Su, X., Song, J. N., & Luo, S. (2025). User attack behavior detection based on PoS consensus. Artificial Intelligence and Robotics Research, 14(4), 855–867.
Liu, D. H., & Xie, J. R. (2025). Artificial intelligence based mobile Internet information encryption technology and network security technology. Information Recording Materials, 26(8), 166–168.
Qian, C. (2023). Smart contract vulnerability detection based on deep learning. Southeast University.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). New York.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Weber, M., Domeniconi, G., Chen, J., et al. (2019). Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. In KDD Workshop on Anomaly Detection in Finance. Anchorage.
Feist, J., Grieco, G., & Groce, A. (2019). Slither: A static analysis framework for smart contracts. In 2019 IEEE/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB) (pp. 8–15). Montreal.
SWC Registry. (2019). Smart Contract Weakness Classification and Test Cases. https://swcregistry.io.
Chawla, N. V., Bowyer, K. W., Hall, L. O., et al. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.