Vulnerability and Transaction behavior based detection of Malicious Smart Contracts

Agarwal, Rachit; Thapliyal, Tanmay; Shukla, Sandeep K.

doi:10.48550/arxiv.2106.13422

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Financial security has become a key issue in the blockchain ecosystem, and this article focuses on four types of SCs-Ponzi, honeypot, gambling, and phishing-that target users' wallet balances [11][12][13].…”

Section: Fraud Smart Contractmentioning

confidence: 99%

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

Zhou,

Yang,

Wang

et al. 2023

CMC

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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 F1score 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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Section: Fraud Smart Contractmentioning

confidence: 99%

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

Zhou,

Yang,

Wang

et al. 2023

CMC

View full text Add to dashboard Cite

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A Comparative Evaluation of Deep Learning Techniques for Smart Contract Vulnerability Classification

Rossini,

Ferretti

2024

Distrib. Ledger Technol.

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Smart contracts are self-executing digital contracts that run on a blockchain network. They enable the automation and decentralization of various operations and have become increasingly popular in recent years. However, smart contracts are susceptible to vulnerabilities, and their deployment without proper security testing can result in severe consequences, such as financial losses and reputational damage. In this paper, we explore the use of deep learning techniques, particularly Convolutional Neural Networks (CNNs), for detecting and classifying vulnerabilities in smart contracts deployed on the Ethereum main net. We compare different kinds of neural architectures, i.e., a baseline LSTM, multiple 1D CNNs working on the smart contracts’ bytecode, a Vision Transformer (Swin v2 Tiny), and various 2D CNNs that work on RGB images obtained from the bytecode (i.e., ResNet-50, ResNeXt-50, Inception v3, and EfficientNetv2 Small). We provide an in-depth analysis of these techniques to classify a dataset of smart contracts we have collected. Our study shows that the use of deep neural networks can represent a promising technique to automatically assess smart contracts’ correctness and classify potential vulnerabilities. According to our experiments, the ResNet 1D CNN working directly on the smart contract bytecode offers the best results in terms of classification capabilities. Moreover, due to the unbalanced sizes of the different classes, the classification resulted in more effectiveness for the unchecked calls and reentrancy vulnerability classes, while still providing good results for others.

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Vulnerability and Transaction behavior based detection of Malicious Smart Contracts

Cited by 2 publications

References 12 publications

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

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

A Comparative Evaluation of Deep Learning Techniques for Smart Contract Vulnerability Classification

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