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Toward Detecting Illegal Transactions on Bitcoin Using Machine-Learning Methods

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 1156)

Abstract

As an emergent electronic payment system, Bitcoin has attracted attention for its desirable features such as disintermediation, decentralization, and tamper-proof recording of data. The Bitcoin network also employs public key cryptography to prevent the disclosure of information related to participating users. Although the public key cryptography ensures the privacy and hides the true identity of users in the Bitcoin network, it has recently been abused for illegal activities that have tarnished the charm of this novel technology. Detecting the illegal transactions associated with illicit activities in Bitcoin is therefore imperative. This paper proposes a machine-learning based approach that classifies Bitcoin transactions as illegal or legal. The detected illegal transactions can be excluded from the subsequent block, promoting user acceptance and adoption of the Bitcoin technology.

Keywords

Bitcoin Illegal transaction detection Classification Bitcoin transaction analysis Transaction feature extraction 
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Notes

Acknowledgments

This work was supported by the ICT R&D program of MSIT/IITP. [No. 2018-0-00539, Development of Blockchain Transaction Monitoring and Analysis Technology] This work was also supported by the ICT R&D program of MSIT/IITP. [No. 2018-0-00749, Development of virtual network management technology based on artificial intelligence].

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2017- 0-01633) supervised by the IITP (Institute for Information & communications Technology Promotion).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPOSTECHPohangKorea

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