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Technology of Blockchain
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2592-2597

Blockchain fraud detection method based on reinforced graph convolutional network and spatiotemporal recurrent gates

Xia Xin
Ren Xiuli
College of Information, Liaoning University, Shenyang 110036, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0616

Abstract

The task of fraud detection in blockchain requires a thorough exploration of the inherent temporal and spatial characteristics in historical transaction data. Existing fraud detection methods suffer from large prediction errors. To address this issue, this paper proposed a blockchain fraud detection method, named RGCN-SRG, based on reinforced graph convolutional network(RGCN) and spatiotemporal recurrent gate(SRG). Firstly, leveraging Bitcoin's historical transaction data for the construction of the transaction graph, the method used a reinforced graph convolutional network with different kernel sizes to comprehensively extract the graph's topology information and generate feature vectors. Additionally, considering the temporal characteristics of blockchain transactions, the method introduced a spatiotemporal recurrent gate structure that incorporated graph convolutional operations into the traditional gate structure to extract dependency information from multiple spatiotemporal dimensions of the transaction graph. Finally, it obtained the prediction results of money laundering detection through a linear layer and activation function. The proposed fraud detection method was evaluated by the constructed dataset. Compared with GCN, DEDGAT, EGT and GCN+MLP F1 by the proposed method improves 18.4, 10.7, 9.2 and 4.9 percentage points, respectively; the precision improves 11.5, 11.2, 7.7 and 3.7 percentage points, respectively.

Foundation Support

辽宁省教育厅资助项目(LYB201617)
国家重点研发计划资助项目(2019YFB1406002)
国家自然科学基金资助项目(61871107)
辽宁省自然科学基金资助项目(201202089)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0616
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Technology of Blockchain
Pages: 2592-2597
Serial Number: 1001-3695(2024)09-004-2592-06

Publish History

[2024-05-07] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

夏鑫, 任秀丽. 基于强化图卷积和时空循环门的区块链非法交易检测方法 [J]. 计算机应用研究, 2024, 41 (9): 2592-2597. (Xia Xin, Ren Xiuli. Blockchain fraud detection method based on reinforced graph convolutional network and spatiotemporal recurrent gates [J]. Application Research of Computers, 2024, 41 (9): 2592-2597. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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