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Detecting Malicious Blockchain Transactions Using Graph Neural Networks

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Distributed Ledger Technology (SDLT 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1975))

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Abstract

The adoption of blockchain technology within various critical infrastructures is on the rise. Concurrently, there has been a corresponding increase in its misuse, primarily through the exploitation of its pseudo-anonymous characteristic. Encouraging blockchain adoption and improving security in the decentralised environment require techniques to detect wallets and/or smart contracts owned by malicious entities. Illegal activities such as dark market trades, money laundering, and receiving unlawful payments are performed by connecting various wallets or smart contracts in a meticulous way. A graph can be a potential representation to visualise such interconnections via various patterns, and graph-based data may represent the topological structure of the blockchain network. Recently, Graph Neural Networks (GNN) have been widely used for analysing the structure of complex networks and identifying patterns. This is the first work that considers a generalised graph representation for the Bitcoin and Ethereum networks and analyses their behaviour using a combination of heterogeneous GNN framework’s GraphSAGE and Graph Attention Network (GAT). The classification results reveal that the proposed approach modestly improved Bitcoin network analysis, whereas Ethereum smart contract analysis needs further investigation in terms of incorporating other aspects of smart contracts, such as code-base, byte length, and lifetime features.

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Author information

Authors and Affiliations

  1. Griffith University, Brisbane, Australia

    Samantha Tharani Jeyakumar, Zhé Hóu & Vallipuram Muthukkumarasamy

  2. University of Jaffna, Jaffna, Sri Lanka

    Andrew Charles Eugene Yugarajah

Authors
  1. Samantha Tharani Jeyakumar
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  2. Andrew Charles Eugene Yugarajah
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  3. Zhé Hóu
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  4. Vallipuram Muthukkumarasamy
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Corresponding authors

Correspondence to Samantha Tharani Jeyakumar , Andrew Charles Eugene Yugarajah , Zhé Hóu or Vallipuram Muthukkumarasamy .

Editor information

Editors and Affiliations

  1. University of Queensland, Brisbane, QLD, Australia

    Naipeng Dong

  2. Southern Cross University, Bilinga, QLD, Australia

    Babu Pillai

  3. University of Queensland, Brisbane, QLD, Australia

    Guangdong Bai

  4. University of Queensland, Brisbane, QLD, Australia

    Mark Utting

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jeyakumar, S.T., Eugene Yugarajah, A.C., Hóu, Z., Muthukkumarasamy, V. (2024). Detecting Malicious Blockchain Transactions Using Graph Neural Networks. In: Dong, N., Pillai, B., Bai, G., Utting, M. (eds) Distributed Ledger Technology. SDLT 2023. Communications in Computer and Information Science, vol 1975. Springer, Singapore. https://doi.org/10.1007/978-981-97-0006-6_4

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  • DOI: https://doi.org/10.1007/978-981-97-0006-6_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0005-9

  • Online ISBN: 978-981-97-0006-6

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