Attention-based model design for Ethereum fraud detection with neural network architecture optimization using Artificial Bee Colony algorithm

Abstract

Fraud detection within the Ethereum network remains a major research challenge due to the strong statistical resemblance between legitimate and fraudulent transaction patterns, severe class imbalance, and the multiscale complexity of temporal-interaction dependencies. Proposing and evaluating a multi-branch attention-based system with automated architecture optimization, which can detect fraudulent Ethereum accounts with high accuracy, is the aim of this study. The experimental evaluation was performed on a dataset with 9,841 samples and 17 extracted features. The proposed system employed a hybrid multi-branch architecture combining CNN, Bi-LSTM, and LSTM with a Gated Fusion mechanism along with multiscale attention layers. The Artificial Bee Colony (ABC) algorithm was applied to automatically optimize sixteen key structural and learning parameters. The results indicate that the proposed system achieved an accuracy of 99.84 %, F1 score of 98.94 %, sensitivity of 98.76 percent, and precision of 99.12 percent. These results notably outperform eight algorithms, such as Random Forest, XGBoost, LGBM, and GADL. According to the confusion matrix analysis, there is a reduction in false negatives, confirming that the system produced only five such cases in the sample set. These findings show that the proposed system is an effective and efficient approach for detecting fraud in blockchain systems and enables deployment in exchanges, DeFi platforms, and regulatory institutions.