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LGBM: a machine learning approach for Ethereum fraud detection

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Abstract

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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Authors and Affiliations

  1. School of Advanced Sciences and Languages (SASL), Mathematics, VIT Bhopal University, Bhopal- Indore Highway, Kothrikalan, Sehore, MP, 466116, India

    Rabia Musheer Aziz & Abdul Hamid Ganie

  2. Basic Science Department, College of Science and Theoretical Studies, Abha Male, Saudi Electronic University, Riyadh, Saudi Arabia

    Rabia Musheer Aziz

  3. School of Computing Science and Engineering, VIT Bhopal University, Bhopal- Indore Highway, Kothrikalan, Sehore, MP, 466116, India

    Mohammed Farhan Baluch & Sarthak Patel

Authors
  1. Rabia Musheer Aziz
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  2. Mohammed Farhan Baluch
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  3. Sarthak Patel
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  4. Abdul Hamid Ganie
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Correspondence to Rabia Musheer Aziz.

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Aziz, R.M., Baluch, M.F., Patel, S. et al. LGBM: a machine learning approach for Ethereum fraud detection. Int. j. inf. tecnol. 14, 3321–3331 (2022). https://doi.org/10.1007/s41870-022-00864-6

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  • DOI: https://doi.org/10.1007/s41870-022-00864-6

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