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Directed dynamic attribute graph anomaly detection based on evolved graph attention for blockchain

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Abstract

Blockchain is gradually becoming an important data storage platform for Internet digital copyright confirmation, electronic deposit, and data sharing. Anomaly detection on the blockchain has received extensive attention as the foundation for securing blockchain-based digital applications. However, the current blockchain anomaly detection for obtaining network nodes’ depth and dynamic change features still needs improvement. In this paper, we propose a public blockchain anomaly detection method based on evolved graph attention. Different from general blockchain network modeling methods, we first adopt a dynamic attribute graph network construction method to model each transaction using edges to provide more learnable transaction attribute information for graph representation learning in blockchain networks. Then, we propose an evoluted graph attention network structure to fully extract the deep features of blockchain nodes by learning the temporal evolution characteristics of blockchain networks and dynamically updating the node learning weights of subgraphs in different timestamps. In order to solve the dataset imbalance problem, we also apply the GraphSMOTE method for graph-structured data on public blockchain networks for the first time. Finally, we identify node labels in blockchain networks using a binary classification method and verify our proposed scheme through multiple rounds of experiments.

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Data and materials are available from the corresponding author on reasonable request.

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Acknowledgements

We wish to thank all data providers. We also wish to thank all colleagues, reviewers and editors provided valuable suggestions.

Funding

This work is supported by the National Natural Science Foundation of China (No. 61972208 and No. 62272239), Postgraduate Research and Innovation Plan of Jiangsu Province (No. KYCX20_0761), Jiangsu Agriculture Science and Technology Innovation Fund (No. CX(22)1007) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 22KJB520027).

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Author notes

  1. Yuhua Xu and Zhixin Sun have contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, New Mofan Road No. 66, Nanjing, 210003, Jiangsu, China

    Chenlei Liu & Zhixin Sun

  2. Post Big Data Technology and Application Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing University of Posts and Telecommunications, New Mofan Road No. 66, Nanjing, 210003, Jiangsu, China

    Yuhua Xu

  3. Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, New Mofan Road No. 66, Nanjing, 210003, Jiangsu, China

    Yuhua Xu

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  1. Chenlei Liu
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Contributions

Chenlei Liu developed idea, performed research, did the analyses and wrote the manuscript. Yuhua Xu did the analyses and collected data. Zhixin Sun verified and revised the manuscript.

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Correspondence to Zhixin Sun.

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Liu, C., Xu, Y. & Sun, Z. Directed dynamic attribute graph anomaly detection based on evolved graph attention for blockchain. Knowl Inf Syst 66, 989–1010 (2024). https://doi.org/10.1007/s10115-023-02033-y

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