Open Access

ARTICLE

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

Xuanchen Zhou^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3

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:

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

Received 10 March 2023; Accepted 19 May 2023; Issue published 26 December 2023

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.

---

Keywords

---