Analyzing Blockchain Data to Detect Bitcoin Addresses Involved in Illicit Activities Using Anomaly Detection

Sarthak Sharan; Divye Sancheti; G. Shobha; Jyoti Shetty; Arjuna Chala; Hugo Watanuki

doi:10.1007/978-981-99-7817-5_11

Analyzing Blockchain Data to Detect Bitcoin Addresses Involved in Illicit Activities Using Anomaly Detection

Sarthak Sharan, Divye Sancheti, G. Shobha, Jyoti Shetty, Arjuna Chala, Hugo Watanuki

HPCC Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The popularity of cryptocurrency has continued to spike in recent years, and among them, bitcoin still remains the most popular. Perhaps the biggest reason for its popularity is the blockchain technology that it is built on. While the technology prevents fraud on the network, there are no checks to track how the bitcoins are being used and for what purpose. Our study tries to investigate the block data stored on the bitcoin blockchain to gain insight and build relationships between transactions that can shed light on the transactions and identify the bitcoin addresses involved in illicit activities. This is carried out by using the HPCC systems analytics platform for ingesting the data. Anomaly detection technique has been used by using a set of specialized features based on transaction behavior where anomalies in users are examined as opposed to anomalies in individual addresses. The K-means algorithm has been used for clustering of data. This study successfully yielded addresses which were potentially involved in illicit activities including involvement in the Mt. Gox hack of 2014.

Original language	English
Title of host publication	Data Science and Applications - Proceedings of ICDSA 2023
Editors	Satyasai Jagannath Nanda, Rajendra Prasad Yadav, Amir H. Gandomi, Mukesh Saraswat
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	137-147
Number of pages	11
ISBN (Print)	9789819978168
DOIs	https://doi.org/10.1007/978-981-99-7817-5_11
State	Published - 2024
Event	4th International Conference on Data Science and Applications, ICDSA 2023 - Jaipur, India Duration: Jul 14 2023 → Jul 15 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	820
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	4th International Conference on Data Science and Applications, ICDSA 2023
Country/Territory	India
City	Jaipur
Period	07/14/23 → 07/15/23

Keywords

Anomaly detection
Bitcoin
Blockchain
HPCC systems
K-means clustering

Access to Document

10.1007/978-981-99-7817-5_11

Cite this

Sharan, S., Sancheti, D., Shobha, G., Shetty, J., Chala, A., & Watanuki, H. (2024). Analyzing Blockchain Data to Detect Bitcoin Addresses Involved in Illicit Activities Using Anomaly Detection. In S. J. Nanda, R. P. Yadav, A. H. Gandomi, & M. Saraswat (Eds.), Data Science and Applications - Proceedings of ICDSA 2023 (pp. 137-147). (Lecture Notes in Networks and Systems; Vol. 820). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-7817-5_11

Sharan, Sarthak ; Sancheti, Divye ; Shobha, G. et al. / Analyzing Blockchain Data to Detect Bitcoin Addresses Involved in Illicit Activities Using Anomaly Detection. Data Science and Applications - Proceedings of ICDSA 2023. editor / Satyasai Jagannath Nanda ; Rajendra Prasad Yadav ; Amir H. Gandomi ; Mukesh Saraswat. Springer Science and Business Media Deutschland GmbH, 2024. pp. 137-147 (Lecture Notes in Networks and Systems).

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abstract = "The popularity of cryptocurrency has continued to spike in recent years, and among them, bitcoin still remains the most popular. Perhaps the biggest reason for its popularity is the blockchain technology that it is built on. While the technology prevents fraud on the network, there are no checks to track how the bitcoins are being used and for what purpose. Our study tries to investigate the block data stored on the bitcoin blockchain to gain insight and build relationships between transactions that can shed light on the transactions and identify the bitcoin addresses involved in illicit activities. This is carried out by using the HPCC systems analytics platform for ingesting the data. Anomaly detection technique has been used by using a set of specialized features based on transaction behavior where anomalies in users are examined as opposed to anomalies in individual addresses. The K-means algorithm has been used for clustering of data. This study successfully yielded addresses which were potentially involved in illicit activities including involvement in the Mt. Gox hack of 2014.",

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author = "Sarthak Sharan and Divye Sancheti and G. Shobha and Jyoti Shetty and Arjuna Chala and Hugo Watanuki",

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Sharan, S, Sancheti, D, Shobha, G, Shetty, J, Chala, A & Watanuki, H 2024, Analyzing Blockchain Data to Detect Bitcoin Addresses Involved in Illicit Activities Using Anomaly Detection. in SJ Nanda, RP Yadav, AH Gandomi & M Saraswat (eds), Data Science and Applications - Proceedings of ICDSA 2023. Lecture Notes in Networks and Systems, vol. 820, Springer Science and Business Media Deutschland GmbH, pp. 137-147, 4th International Conference on Data Science and Applications, ICDSA 2023, Jaipur, India, 07/14/23. https://doi.org/10.1007/978-981-99-7817-5_11

Analyzing Blockchain Data to Detect Bitcoin Addresses Involved in Illicit Activities Using Anomaly Detection. / Sharan, Sarthak; Sancheti, Divye; Shobha, G. et al.
Data Science and Applications - Proceedings of ICDSA 2023. ed. / Satyasai Jagannath Nanda; Rajendra Prasad Yadav; Amir H. Gandomi; Mukesh Saraswat. Springer Science and Business Media Deutschland GmbH, 2024. p. 137-147 (Lecture Notes in Networks and Systems; Vol. 820).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Sharan, Sarthak

AU - Sancheti, Divye

AU - Shobha, G.

AU - Shetty, Jyoti

AU - Chala, Arjuna

AU - Watanuki, Hugo

PY - 2024

Y1 - 2024

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AB - The popularity of cryptocurrency has continued to spike in recent years, and among them, bitcoin still remains the most popular. Perhaps the biggest reason for its popularity is the blockchain technology that it is built on. While the technology prevents fraud on the network, there are no checks to track how the bitcoins are being used and for what purpose. Our study tries to investigate the block data stored on the bitcoin blockchain to gain insight and build relationships between transactions that can shed light on the transactions and identify the bitcoin addresses involved in illicit activities. This is carried out by using the HPCC systems analytics platform for ingesting the data. Anomaly detection technique has been used by using a set of specialized features based on transaction behavior where anomalies in users are examined as opposed to anomalies in individual addresses. The K-means algorithm has been used for clustering of data. This study successfully yielded addresses which were potentially involved in illicit activities including involvement in the Mt. Gox hack of 2014.

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A2 - Yadav, Rajendra Prasad

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Sharan S, Sancheti D, Shobha G, Shetty J, Chala A, Watanuki H. Analyzing Blockchain Data to Detect Bitcoin Addresses Involved in Illicit Activities Using Anomaly Detection. In Nanda SJ, Yadav RP, Gandomi AH, Saraswat M, editors, Data Science and Applications - Proceedings of ICDSA 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 137-147. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-7817-5_11

Analyzing Blockchain Data to Detect Bitcoin Addresses Involved in Illicit Activities Using Anomaly Detection

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