Exploiting Deep Learning Based Classification Model for Detecting Fraudulent Schemes over Ethereum Blockchain

Abstract

The extensive usage of the Blockchain technology as one of the most popular forms of decentralized platform has been spread across a numerous field over the recent years. From financial sectors like banking industry to the supply chain management of multiple large corporate farms, blockchain technology has been proven its productivity across different communities. However, the reliability of the blockchain system has often been compromised with the introduction of various scams and fraudulent activity within the system. Due to the absence of a comprehensive and definitive dataset, the challenges of building an effective fraud detection model becomes even more sever in this particular field. Thus, in our work, we propose a deep learning based blockchain fraud detection model based on the Ethereum blockchain transaction data. With the association of a reliable dataset in this field, we build a deep learning-based detection model to classify the fraudulent activities within the system. The proposed classification model is comprised of a Long Short-Term Memory (LSTM) unit and a dense unit to detect the fraudulent transactions. For the sake of reducing down the complexity and avoiding the unnecessary transactional features, Information Gain has been utilized as the feature selection unit of the model. When compared to the corresponding values of different models on the same dataset, experimental results show a significant improved results in different aspects using the proposed approach.