Preserving Security of Crypto Transactions with Machine Learning Methodologies

In recent years, with the increasing popularity of cryptocurrencies, the number of fraudulent activities in the crypto space has also risen. To combat this, machine learning techniques have been developed for detecting fraudulent transactions. This study, compares the performance of three popular machine learning algorithms, Logistic Regression, Random Forest Classifier, and XGB Classifier, for detecting crypto fraud. And they utilize a dataset containing features related to the transaction, such as the time of the transaction, the amount involved, and the country of origin. The results show that all three algorithms achieve high accuracy in detecting fraud, with XGB Classifier outperforming the other two. These results show that all three algorithms are capable of detecting fraudulent activities with high accuracy, with XGB Classifier outperforming the other two. This study provides valuable insights into the use of machine learning in detecting fraudulent activities within the cryptocurrency industry and highlights the potential of these algorithms for future research in this area.