Ethereum Fraud Detection Using Machine Learning

Blockchain technologies for cryptocurrency applications have gotten a lot of attention and popularity among researchers and public consumers in recent years. The advent of cryptocurrency technology has defined the feature of trading currencies without a central authority. The anonymity nature of blockchain cryptocurrencies has caused a rise in illegal activities such as money laundering, ransomware attack payments, dark web payment transactions and cryptocurrencies could be one of the largest unregulated financial markets. This research focuses mainly on the second-largest cryptocurrency by market capitalization known as Ethereum to develop a prevention fraud detection model to detect any forms of illegal or fraudulent transactions in the markets. The experimented predictive models are Random Forest, Support Vector Machines, and K Nearest Neighbour. From the experiments, Random Forest achieved the highest f1 score of 0.98 amongst the three predictive models.