Detecting Fraudulent Accounts on Blockchain: A Supervised Approach

Michał Ostapowicz , Kamil Piotr Żbikowski

Abstract

Applications of blockchain technologies got a lot of attention in recent years. They exceed beyond exchanging value and being a substitute for fiat money and traditional banking system. Nevertheless, being able to exchange value on a blockchain is at the core of the entire system and has to be reliable. Blockchains have built-in mechanisms that guarantee whole system’s consistency and reliability. However, malicious actors can still try to steal money by applying well known techniques like malware software or fake emails. In this paper we apply supervised learning techniques to detect fraudulent accounts on Ethereum blockchain. We compare capabilities of Random Forests, Support Vector Machines and XGBoost classifiers to identify such accounts basing on a dataset of more than 300 thousands accounts. Results show that we are able to achieve recall and precision values allowing for the designed system to be applicable as an anti-fraud rule for digital wallets or currency exchanges. We also present sensitivity analysis to show how presented models depend on particular feature and how lack of some of them will affect the overall system performance.
Author Michał Ostapowicz
Michał Ostapowicz,,
-
, Kamil Piotr Żbikowski (FEIT / IN)
Kamil Piotr Żbikowski,,
- The Institute of Computer Science
Pages18-31
Publication size in sheets0.65
Book Cheng Reynold, Mamoulis Nikos, Sun Yizhou, Huang Xin (eds.): Web Information Systems Engineering – WISE 2019 20th International Conference, Hong Kong, China, November 26–30, 2019, Proceedings, Lecture Notes In Computer Science, vol. 11881, 2019, Springer, ISBN 978-3-030-34222-7, [978-3-030-34223-4 (eBook) ], 832 p., DOI:10.1007/978-3-030-34223-4
Keywords in EnglishBlockchain Anti-fraud Supervised Xgboost Random forests SVM Ethereum
DOIDOI:10.1007/978-3-030-34223-4_2
URL https://link.springer.com/chapter/10.1007%2F978-3-030-34223-4_2
Languageen angielski
File
978-3-030-34223-4_2.pdf 537.89 KB
Score (nominal)140
Score sourceconferenceList
ScoreMinisterial score = 140.0, 16-01-2020, ChapterFromConference
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