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36864.pdf (1.26 MB)
ETD Abstract Container
Abstract Header
Anomaly Detection in Ethereum Transactions Using Network Science Analytics
Author Info
Lawal, Yusuf Lanre
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=ucin159585057190135
Abstract Details
Year and Degree
2020, MS, University of Cincinnati, Education, Criminal Justice, and Human Services: Information Technology.
Abstract
Since the introduction of Bitcoin, the rate of adoption of blockchain technology has exponentially increased. Consequently, numerous other types of cryptocurrencies, such as Ethereum, have been introduced. The high rate of adoption of cryptocurrencies has resulted in the generation of enormous amounts of data. In this paper, we focus on detecting anomaly or outliner in the daily Ethereum network using network properties. We were able to use the network properties and data mining in getting the required results. Wallets or accounts acting on the blockchain are represented as nodes, while interactions between wallets or accounts are represented as links or edges. Based on the explanation, we were able to discover how the network properties have an impact on transaction behavior within the network. we propose how this analysis would be useful in real-life events.
Committee
Bilal Gonen, Ph.D. (Committee Chair)
Kijung Lee, Ph.D. (Committee Member)
Pages
53 p.
Subject Headings
Information Technology
Keywords
Ethereum
;
Network Science
;
Blockchain
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Citations
Lawal, Y. L. (2020).
Anomaly Detection in Ethereum Transactions Using Network Science Analytics
[Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin159585057190135
APA Style (7th edition)
Lawal, Yusuf Lanre.
Anomaly Detection in Ethereum Transactions Using Network Science Analytics.
2020. University of Cincinnati, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=ucin159585057190135.
MLA Style (8th edition)
Lawal, Yusuf Lanre. "Anomaly Detection in Ethereum Transactions Using Network Science Analytics." Master's thesis, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin159585057190135
Chicago Manual of Style (17th edition)
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Document number:
ucin159585057190135
Download Count:
1,340
Copyright Info
© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.