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The Detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

Xuanchen Zhou1,2,3, Wenzhong Yang2,3,*, Liejun Wang2,3, Fuyuan Wei2,3, KeZiErBieKe HaiLaTi2,3, Yuanyuan Liao2,3

1 College of Software, Xinjiang University, Urumqi, 830000, China
2 Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Urumqi, 830000, China
3 Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region, Urumqi, 830000, China

* Corresponding Author: Wenzhong Yang. Email: email

Computers, Materials & Continua 2023, 77(3), 4073-4087. https://doi.org/10.32604/cmc.2023.040253

Abstract

With the increasing popularity of Ethereum, smart contracts have become a prime target for fraudulent activities such as Ponzi, honeypot, gambling, and phishing schemes. While some researchers have studied intelligent fraud detection, most research has focused on identifying Ponzi contracts, with little attention given to detecting and preventing gambling or phishing contracts. There are three main issues with current research. Firstly, there exists a severe data imbalance between fraudulent and non-fraudulent contracts. Secondly, the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts. Lastly, most prior studies have used contract source code as raw features, but many smart contracts only exist in bytecode. To address these issues, we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet (ECA-EfficientNet) and data enhancement. Our method begins by converting bytecode into Red Green Blue (RGB) three-channel images and then applying channel exchange data enhancement. We then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB images. Our proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi, honeypot, gambling, and phishing smart contracts. The results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract detection. Our research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.

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APA Style
Zhou, X., Yang, W., Wang, L., Wei, F., HaiLaTi, K. et al. (2023). The detection of fraudulent smart contracts based on eca-efficientnet and data enhancement. Computers, Materials & Continua, 77(3), 4073-4087. https://doi.org/10.32604/cmc.2023.040253
Vancouver Style
Zhou X, Yang W, Wang L, Wei F, HaiLaTi K, Liao Y. The detection of fraudulent smart contracts based on eca-efficientnet and data enhancement. Comput Mater Contin. 2023;77(3):4073-4087 https://doi.org/10.32604/cmc.2023.040253
IEEE Style
X. Zhou, W. Yang, L. Wang, F. Wei, K. HaiLaTi, and Y. Liao "The Detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement," Comput. Mater. Contin., vol. 77, no. 3, pp. 4073-4087. 2023. https://doi.org/10.32604/cmc.2023.040253



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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