Deep Learning-Based Anomaly Detection for Fraudulent Transactions in Ethereum Blockchain

Abstract

For this research, a strong framework is suggested for recognizing fraudulent Ethereum transactions by using both ML and DL approaches. As more people adopt Ethereum for DeFi, NFTs and smart contracts, the integrity of the system is being threatened more often. To deal with these difficulties, the work presents a hybrid RFDNN model which is trained over a labeled dataset of 9,841 Ethereum transactions, including 2,179 that are fraudulent. Class imbalance presents a big issue, so an advanced version of the Synthetic Minority Oversampling Technique (ISMOTE) is used to produce better synthetic data samples that make the model more general. To avoid overfitting and increase results in the real world, the methodology stresses feature engineering, data cleaning and adjusting for the right partitions. The research compares how traditional fraud detection works with adaptive ML models and highlights how adapting to new situations can catch more updated as well as advanced fraud. We need to ensure that any automated fraud system is both understandable and can handle large amounts of data for people to put faith in it. This study reveals that staying flexible and re-training models regularly is important due to the growing number of threats. Experiments were done with Logistic Regression (LR), Decision Tree (DT), Gradient Boosting (GB) and XGBoost, with the hybrid RFDNN model reaching the best results: 97% accuracy, 96% precision, 96% recall and a 97% F1 score.