Anomaly Detection in Ethereum Transactions Using Autoencoder Networks

Abstract

The rapid growth of Ethereum blockchain transactions has led to increased vulnerability to fraudulent activities. Traditional rule-based detection systems fail to capture complex and evolving fraud patterns. This paper presents an unsupervised deep learning approach using an Autoencoder to model normal transaction behavior and detect anomalies based on reconstruction error. The model is trained on normal transactions and evaluated on a dataset of 9,841 Ethereum accounts with 45 features. The system achieves a recall of 78.7%, precision of 69%, F1-score of 0.74, and ROC-AUC of 0.70. The proposed approach demonstrates the effectiveness of unsupervised learning for fraud detection without requiring labeled datasets.