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

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ID: 92480
2019
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Abstract
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.
Reference Key
podgorelec2019asensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Podgorelec, Blaž;Turkanović, Muhamed;Karakatič, Sašo;
Journal sensors
Year 2019
DOI
E147
URL
Keywords
Machine learning blockchain anomaly detection digital identity management transactions

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