BiLSTM4DPS: An attention-based BiLSTM approach for detecting phishing scams in ethereum

Abstract

With the burgeoning adoption of blockchain technology, cryptocurrencies have surged in popularity, becoming a focal point of global interest. Concurrently, the emergence of cryptocurrency phishing scams poses a significant threat to the financial security of the blockchain ecosystem, inflicting substantial economic damage on platforms and users alike. This study introduces an innovative approach leveraging an attention-augmented Bidirectional Long Short Term Memory Network (BiLSTM), termed BiLSTM4DPS, for the detection of phishing scams within the Ethereum network. We initiate by converting account transaction records into sequences, thereby extracting temporal and latent patterns of transactions. Subsequently, we integrate BiLSTM with multi-head attention mechanisms and masking techniques to construct a robust classification model aimed at identifying fraudulent accounts. Extensive experiments were conducted to assess the efficacy of BiLSTM4DPS, particularly under scenarios with limited account activity data. The results demonstrate that BiLSTM4DPS achieves remarkable predictive accuracy, surpassing existing state-of-the-art methods.