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Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications

Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications

Ruchi Mittal, M. P. S. Bhatia
Copyright: © 2021 |Volume: 13 |Issue: 1 |Pages: 15
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799860341|DOI: 10.4018/IJDCF.2021010105
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MLA

Mittal, Ruchi, and M. P. S. Bhatia. "Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications." IJDCF vol.13, no.1 2021: pp.79-93. http://doi.org/10.4018/IJDCF.2021010105

APA

Mittal, R. & Bhatia, M. P. (2021). Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications. International Journal of Digital Crime and Forensics (IJDCF), 13(1), 79-93. http://doi.org/10.4018/IJDCF.2021010105

Chicago

Mittal, Ruchi, and M. P. S. Bhatia. "Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications," International Journal of Digital Crime and Forensics (IJDCF) 13, no.1: 79-93. http://doi.org/10.4018/IJDCF.2021010105

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Abstract

In this age, where cryptocurrencies are slowly creeping into the banking services and making a name for them, it is becoming crucially essential to figure out the security concerns when users make transactions. This paper investigates the untrusted users of cryptocurrency transaction services, which are connected using smartphones and computers. However, as technology is increasing, transaction frauds are growing, and there is a need to detect vulnerabilities in systems. A methodology is proposed to identify suspicious users based on their reputation score by collaborating centrality measures and machine learning techniques. The results are validated on two cryptocurrencies network datasets, Bitcoin-OTC, and Bitcoin-Alpha, which contain information of the system formed by the users and the user's trust score. Results found that the proposed approach provides improved and accurate results. Hence, the fusion of machine learning with centrality measures provides a highly robust system and can be adapted to prevent smart devices' financial services.