Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions

Abstract

Cryptocurrency has emerged as a decentralized transaction to overcome the problems of the centralized transaction system. Although it has become a popular trend in online cryptocurrency transactions and mobile wallets, this method has increased the number of fraudulent transactions instead of physically transferring money. Because the shared data and the history of online transactions may lead to fraudulent transactions. The preprocess identification of fraudulent cryptocurrency transactions is becoming an urgent research question. With the exponential blossoming of Artificial Intelligence, the employing of deep learning in predicting social issues has been achieved in many disciplines. From this perspective, this paper proposes an ensemble learning approach for fraudulent cryptocurrency transactions by integrating two deep learning methods: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The off-the-shelf CNN and LSTM, ensemble CNN, and ensemble LSTM with the bagged and boosted approach are compared in terms of accuracy and losses from training and test datasets. Moreover, the 10-fold cross-validation approach is employed for the evaluation of the proposed approach. The evaluation results indicate that the bagged LSTM ensembled approach is significant with 96.4% accuracy and outperforms the other approaches.

Keywords

• Blockchain
• classification
• convolutional neural networks
• cryptocurrency
• long short term memory