Прогнозирование Мошеннических Транзакций С Криптовалютой С Использованием Логистической Регрессии

Abstract

The article conducts a SWOT analysis of cyber threats, identifies the strengths and weaknesses of cryptocurrencies. It is determined that the development of cryptocurrencies forms a new class of digital assets, which is attracting increasing attention from the economic and financial communities and information technology. The issue of detecting fraudulent transactions with cryptocurrency is highlighted. To solve the problems of detecting fraudulent transactions, the authors propose to use new technologies based on data analysis methods, in particular, the development of logistic regression models. The following algorithm for classifying fraudulent transactions with cryptocurrency is proposed, which is reduced to the classical data classification scheme. The following steps are highlighted: data loading into the dataset and primary analysis, data preparation for analysis, division into training and test samples, application of the classification algorithm on the training sample, evaluation of the model accuracy on the test sample, model optimization if necessary, and conclusion, where, if the model accuracy is high, it can be used to classify fraudulent transactions. The main task of the presented stages is to detect suspicious cryptocurrency transactions with as few false positives as possible. The classification of cryptocurrency transactions is proposed to be carried out with the Ethereum cryptocurrency. The R language and its integrated processing environment R Studio are chosen as tools. A logistic regression model has been developed to detect fraudulent transactions with cryptoassets. The model checks a new transaction for fraud. The model's high accuracy of 98 percent demonstrates its effectiveness. The model can be improved to take into account new types of fraudulent schemes and applied to analyze transactions with different assets, making it promising for use in financial institutions and cryptocurrency exchanges.