The phenomenon of cryptocurrencies continues to draw a lot of attention from investors, innovators and the general public. There are over 1300 different cryptocurrencies, including Bitcoin, Ethereum and Litecoin. 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 was analyzed using a graph traversal algorithm. 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. Research results revealed that the best performance was achieved using a random forest classification model .

Cryptocurrency are difficult to forecast due to it’s globality and availability to everyone and every time. There is no Friday or Holidays effect, seasonality, market news and other aspects, which influence the course direction. It is the phenomena of the market and it is useful to spread forecast methods research to find out the best fitting model for this phenomenon. In this paper is presented short-term forecast of five different cryptocurrencies (Bitcoin, BitcoinCash, Ethereum, Litecoin, Ripple). Forecast methods split in two groups: 1) real value (ARIMA and SVR models) 2) volatility (GARCH and SVR models). The model’s suitability is evaluated by RMSE and MAE. The best results for real value forecast were achieved using ARIMA, for volatility forecast - SVR. In further research it would be useful to analyze methods variety of Artificial Neural Networks and others connected models’ modifications.