A novel approach to detect fraud in Ethereum transactions using stacking

• Published in: Expert systems Vol. 40; no. 7
• Main Authors: Md, Abdul Quadir, Narayanan, S. M. Satya Sree, Sabireen, H., Sivaraman, Arun Kumar, Tee, Kong Fah
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.08.2023
• Subjects: Algorithms; block chain; Blockchain; Classifiers; cryptocurrency; Currency transactions; Decision trees; Digital currencies; ensemble; ether; Ethereum; Expert systems; Fraud; fraud detection; Fraud prevention; Machine learning; Regression analysis; Stacking; stacking classifier
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.13255

Ever since Ether is launched as a digital currency, its rise has been rapid. It is currently the second most valuable digital currency in the world. There are more than 1 million transactions happening on the Ethereum network every day, and this number is expected to continue to increase. Due to the increasing number of transactions, fraudulent transactions have also increased, which has resulted in a large amount of money being lost and has also destroyed the livelihoods of many individuals. Due to their similarity to valid transactions, it is extremely difficult to distinguish between them. Additionally, Ethereum's pseudo‐anonymity adds to the difficulty of identifying the parties involved. Since there are millions of transactions every day, it would be difficult to manually verify each one. Therefore, a mechanism for validating these transactions is needed. In this context, this paper proposes a novel approach to detecting fraudulent accounts associated with these transactions by implementing machine learning algorithms among the given set of transactions. We propose a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta‐learner based on the output of each base algorithm. The algorithms include Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts. As a result of combining these algorithms, a powerful classifier with the ability to detect fraudulent transactions. A variety of machine learning models were trained and evaluated on the test set using various metrics. Based on the results of the individual algorithm the Random Forest algorithm achieved the highest accuracy of 95.47%, followed by Gradient Boosting at 94.61% which is an ensemble algorithm using the boosting technique. The Stacking classifier that combines Multinomial Naive Bayes and Random Forest as the base learners and logistic regression as the Meta learner achieved the highest accuracy of 97.18% with an F1 score of 97.02%. Based on the results of all the stacking models developed, it is concluded that algorithms tend to perform better when combined properly. When compared to the other approaches, the proposed approach has outperformed the others, making it feasible in the real world to detect fraudulent transactions.