Intelligent Fraud Detection in Ethereum Transactions Using Machine Learning

Abstract

As Ethereum continues to gain traction as a leading blockchain platform, its open and decentralized nature has also made it an attractive target for fraudulent activities. This paper presents a machine learning-based approach to detect fraudulent Ethereum transactions by analyzing behavioral patterns within transaction data. Using a labeled dataset of Ethereum transactions, various classification algorithms such as Random Forest, XGBoost, and Support Vector Machines were trained and evaluated. The proposed system focuses on identifying anomalies and suspicious transaction behavior by extracting relevant features like gas usage, transaction value, and timing. Experimental results show that the model can achieve high accuracy and precision in distinguishing between legitimate and fraudulent transactions. This work contributes to the growing field of blockchain security by demonstrating the viability of intelligent fraud detection techniques and providing a framework that can be integrated into real-world applications.

Introduction

The rapid growth of blockchain technology, especially Ethereum, has revolutionized digital finance by enabling decentralized, peer-to-peer transactions via smart contracts. However, this openness also exposes the platform to various fraudulent activities like phishing and contract exploitation. Detecting fraud on Ethereum is challenging due to transaction complexity, scale, and anonymity, and traditional methods often fail to keep up.

This research focuses on improving fraud detection using machine learning (ML) techniques applied to Ethereum transaction data. By analyzing features extracted from public datasets, supervised learning models such as Random Forest, XGBoost, Logistic Regression, and SVM are evaluated for their ability to classify transactions as fraudulent or legitimate.

The literature review highlights the evolution from rule-based and anomaly detection methods to ML and deep learning models, including graph-based approaches, noting persistent challenges like scalability, adaptability, and data labeling.

The proposed system architecture involves stages of data acquisition, preprocessing, feature engineering, model training, and real-time fraud detection with alerting mechanisms.

Datasets are collected from sources like Etherscan, Kaggle, and custom nodes, with preprocessing steps including normalization, encoding, and balancing to prepare data for training.

Methodology emphasizes data cleaning, feature selection, and testing multiple ML models with cross-validation and evaluation using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.

Results show that XGBoost outperforms other models with up to 98% accuracy after hyperparameter tuning, demonstrating promising effectiveness in identifying fraudulent Ethereum transactions.

Conclusion

This research presents an intelligent fraud detection system for Ethereum transactions using advanced machine learning techniques, emphasizing the growing need to address fraudulent activity in blockchain networks. Among the models evaluated, XGBoost consistently demonstrated superior performance, offering high precision and recall in identifying fraudulent transactions within an imbalanced dataset. The study confirms that machine learning models, when properly tuned and supported by robust data preprocessing and feature engineering, can effectively uncover complex patterns in blockchain data, making them suitable for enhancing transactional security. However, challenges such as evolving fraud strategies, data imbalance, and limitations in generalizability highlight the need for continuous refinement. Despite these challenges, the study provides valuable insights into the application of machine learning for fraud detection and sets the foundation for more sophisticated security solutions in blockchain ecosystems

References

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Copyright

Copyright © 2025 Mr. V. Satish, Sapa Nithin Sai Ganesh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.