Abstract
Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%.
Highlights
The Blockchain is a distributed database, known as a digital ledger
The banking and financial services industry has recognized the value of blockchain technology, which helps customers in safe transactions
Four performance metrics are used in this paragraph to measure the performance of the method mentioned below
Summary
The Blockchain is a distributed database, known as a digital ledger. Due to various factors, such as high compatibility with financial systems and the ability to support smart contracts [1], blockchain technology in banking and financial services is expected to expand rapidly around the world. The banking and financial services industry has recognized the value of blockchain technology, which helps customers in safe transactions. All the data on a blockchain are recorded accurately and can serve as a history that is available for all applications [2]. A blockchain is a constantly developing computerized record in pieces known as blocks which are connected and verified utilizing cryptographic hash functions. Due to its innovative characteristics, fresh applications are usually created incessantly over its framework
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