Machine Learning Approach for Cryptocurrency Fraud Detection
Keywords:
Cryptocurrency, Ethereum, Feature selection, Fraud detection, Machine learningAbstract
Fraud detection is an important aspect of maintaining the security and integrity of the Ethereum blockchain network. Ethereum is a decentralized blockchain platform that has gained widespread adoption for its smart contract functionality. This paper proposes a two-stage machine-learning approach for detecting fraudulent activities on the Ethereum network. In the first stage, we apply SMOTE to balance the dataset, then baseline accuracies are obtained more accurately. In the second stage, we rank the features according to the F_regression algorithm. We evaluate the feature subsets performances of our model on the balanced Kaggle Ethereum fraud detection dataset using logistic regression, Gaussian Naive Bayes, and support vector machine algorithms. Our results show that using the Gaussian Naïve Bayes machine learning method to detect fraud on the Ethereum network gives the highest accuracy.
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