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Soft Computing: Theories and Applications pp 25–32Cite as

Detection of Bitcoin Miners by Clustering Crypto Address with Google BigQuery Open Dataset

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 425))

Abstract

Bitcoin blockchain is a financial transaction network and the bitcoin miners use pseudonymous crypto addresses. The miners use their hash rate or computational power to mine the next block. After successful mining, the miners get rewards and the block gets appended to the main canonical blockchain. These addresses are used by bitcoin miners to send the cryptocurrency from one address to another without revealing the true identity. We have used a clustering algorithm to find the ownership of these addresses. Google BigQuery dataset Crypto\(\_\)bitcoin is used, and the data frame is got by running SQL query in Kaggle notebook. The results show four promising cluster aggregations with the appropriate centroids. The elbow method is used to determine the number of clusters, and the metric within-cluster sum of squares (WCSS) is also calculated. Thus, we can show that a simple K-means clustering algorithm can be used to detect the relationship between the addresses and miners. These clusters show the group of miners with similar computational power or hash rate. Thus, these pseudonymous crypto addresses can be linked to the bitcoin miners with a similar hash rate. The clusters are validated with Silhouette scores and the results are visualized.

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Author information

Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thanjavur, 613401, India

    M. J. Jeyasheela Rakkini & K. Geetha

Authors
  1. M. J. Jeyasheela Rakkini
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  2. K. Geetha
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Corresponding author

Correspondence to M. J. Jeyasheela Rakkini .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

  2. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Chang Wook Ahn

  3. Department of Computer Science, Shobhit University, Gangoh, India

    Tarun K. Sharma

  4. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Om Prakash Verma

  5. Indian Institute of Information Technology Kota, Jaipur, Rajasthan, India

    Anand Agarwal

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Jeyasheela Rakkini, M.J., Geetha, K. (2022). Detection of Bitcoin Miners by Clustering Crypto Address with Google BigQuery Open Dataset. In: Kumar, R., Ahn, C.W., Sharma, T.K., Verma, O.P., Agarwal, A. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 425. Springer, Singapore. https://doi.org/10.1007/978-981-19-0707-4_3

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