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Open AccessArticle

A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection

Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
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Sensors 2020, 20(1), 147; https://doi.org/10.3390/s20010147
Received: 29 November 2019 / Revised: 20 December 2019 / Accepted: 22 December 2019 / Published: 25 December 2019
(This article belongs to the Special Issue Artificial Intelligence and Blockchain in Wireless Sensors Networks)
The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions. View Full-Text
Keywords: blockchain; transactions; digital identity management; anomaly detection; machine learning blockchain; transactions; digital identity management; anomaly detection; machine learning
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Podgorelec, B.; Turkanović, M.; Karakatič, S. A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection. Sensors 2020, 20, 147.

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