Crypto metrics

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Ethereum;Cryptocurrency;Blockchain;Fraudulent activity;K-Means clustering;Support vector machine;Random forest classifier Crypto metrics The phenomenon of cryptocurrencies continues to draw a lot of attention from investors, innovators and the general public. There are over different cryptocurrencies, including Bitcoin, Ethereum and Litecoin.

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While the scope of blockchain technology and cryptocurrencies continues to increase, identification of unethical and fraudulent behaviour still remains an open issue. The absence of regulation of the cryptocurrencies ecosystem and the lack of transparency of the transactions may lead to an increased number of fraudulent cases.

In this research, we have analyzed the possibility to identify fraudulent behaviour using different classification techniques. Based on Etherium transactional data, we constructed a transaction network which crypto metrics analyzed using a graph traversal algorithm.

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Data clustering was performed using three machine learning algorithms: k-means clustering, Support Vector Machine and random forest classifier. The performance of the classifiers was evaluated using a few accuracy metrics that can be calculated from confusion matrix.

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Research results revealed that the best performance was achieved using a random forest classification model Internet:.

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