LGBM: a machine learning approach for Ethereum fraud detection

计算机科学 机器学习 梯度升压 随机森林 人工智能 Boosting(机器学习) 感知器 网络钓鱼 多层感知器 数据库事务 数据挖掘
作者
Rabia Aziz,Mohammed Farhan Baluch,Sarthak Patel,Abdul Hamid Ganie
出处
期刊:International journal of information technology [Springer Nature]
标识
DOI:10.1007/s41870-022-00864-6
摘要

Ethereum is a software platform that uses the concept of blockchain and decentralizes data by distributing copies of smart contracts to thousands of individuals worldwide. Ethereum, as a currency, is utilized to exchange value worldwide in the absence of a third party to monitor or intervene. However, as online commerce grows, a slew of fraudulent activities, such as money laundering, bribery, and phishing, emerge as the primary threat to trade security. This paper proposes Light Gradient Boosting Machine (LGBM) approach for accurately detecting fraudulent transactions. It also examines different models such as Random Forest (RF), Multi-Layer Perceptron (MLP), etc., based on machine learning and soft computing algorithm for classifying Ethereum fraud detection dataset with limited attributes and compares their metrics with the LGBM approach. A comparative study of scores of bagging models is presented to know the applicability of the proposed approach. The light gradient boosting machine (LGBM) algorithms and Extreme Gradient Boosting (XGBoost) demonstrate the highest accuracies, while LGBM shows slightly better performance with 98.60% for the stated dataset scenarios. Further optimizing the LGBM with hyper-parameter tuning, an accuracy of 99.03% is achieved.
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