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Graph Neural Network-Based Anomaly Detection in Blockchain Network

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Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security (CCCS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 664))

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Abstract

Long-term research has been done on anomaly identification. Its uses in the banking industry have made it easier to spot questionable hacker activity. However, it is more difficult to trick financial systems due to innovations in the financial sector like blockchain and artificial intelligence. Despite these technical developments, there have nevertheless been several instances of fraud. To address the anomaly detection issue, a variety of artificial intelligence algorithms have been put forth; while some findings seem to be remarkably encouraging, no clear winner has emerged. In order to identify fraudulent transactions, this article presented Inspection-L architecture based on graph neural network (GNN) with self-supervised deep graph infomax (DGI) and graph isomorphism network (GIN), with supervised knowledge methods, such as random forest (RF). The potential of self-supervised GNN in Bitcoin unlawful transaction detection has been demonstrated by the evaluation of the proposed technique on the Elliptic dataset. Results from experiments reveal that our approach outperforms existing standard methods for detecting anomalous events.

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Acknowledgements

The research is supported by postdoctoral fellowship granted by the Institute of Computer Technologies and Information Security, Southern Federal University, project No PD/22-02-KT.

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Authors and Affiliations

  1. Institute of Computer Technologies and Informational Security, Southern Federal University, Rostov-on-Don, 344006, Russia

    Amit Sharma, Elizaveta Podoplelova & Vadim Gavrilenko

  2. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Amit Sharma

  3. Narsee Monjee Institute of Management Studies (NMIMS), STME, Chandigarh, India

    Pradeep Kumar Singh

  4. Department of Information and Analytical Security Systems, Southern Federal University, Rostov-On-Don, 344006, Russia

    Alexey Tselykh

  5. Southern Federal University, 44 Nekrasovsky Street, Taganrog, 347922, Russia

    Alexander Bozhenyuk

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  1. Amit Sharma
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  2. Pradeep Kumar Singh
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  3. Elizaveta Podoplelova
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  4. Vadim Gavrilenko
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  5. Alexey Tselykh
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  6. Alexander Bozhenyuk
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Corresponding author

Correspondence to Amit Sharma .

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Editors and Affiliations

  1. Department of Computer Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Slawomir T. Wierzchon

  3. Department of Computer Science and Engineering, KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  4. Faculty of Mathematics and Informatics, Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

  5. Department of Security Engineering, University of Warwick, Coventry, UK

    Gregory Epiphaniou

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sharma, A., Singh, P.K., Podoplelova, E., Gavrilenko, V., Tselykh, A., Bozhenyuk, A. (2023). Graph Neural Network-Based Anomaly Detection in Blockchain Network. In: Tanwar, S., Wierzchon, S.T., Singh, P.K., Ganzha, M., Epiphaniou, G. (eds) Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security. CCCS 2022. Lecture Notes in Networks and Systems, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-99-1479-1_67

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  • DOI: https://doi.org/10.1007/978-981-99-1479-1_67

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1478-4

  • Online ISBN: 978-981-99-1479-1

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