Abstract

Ethereum, a prominent block chain platform, facilitates digital asset transactions through its decentralized network. However, the decentralized nature of Ethereum also opens avenues for fraudulent activities, presenting challenges to network security and integrity. This work addresses the problem of Ethereum transaction fraud by proposing an advanced fraud detection system. Initially, it outlines the characteristics of Ethereum transactions and the associated risks of fraudulent behavior within the network. Subsequently, it highlights the detrimental impacts of fraudulent transactions, including financial losses and diminished trust among users. To combat these challenges, the work presents a comprehensive solution in the form of a Hybrid Model for Ethereum Fraud Prediction (HMEFP). The proposed HMEFP model integrates a Customized Dense Deep Learning Model and an LSTM-based recurrent neural network, which collectively learn intricate patterns and temporal dependencies within Ethereum transactions. Through a fusion mechanism, the model combines the strengths of both architectures, enhancing its fraud detection capabilities The results demonstrate the potential of the HMEFP to safeguard the integrity and security of the Ethereum network by accurately identifying fraudulent transactions.