利用表格 GAN 在以太坊网络中进行恶意地址分类

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Computer Networks Pub Date : 2024-09-19 DOI:10.1016/j.comnet.2024.110813
Muhammad Ahtazaz Ahsan, Amna Arshad, Adnan Noor Mian
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引用次数: 0

摘要

以太坊在加密货币交易中的流行吸引了恶意行为者参与网络钓鱼、庞氏骗局和赌博等非法活动。由于网络钓鱼地址数量庞大,以往的研究主要集中在网络钓鱼方面。然而,由于庞氏骗局或赌博地址的可用性有限,因此还没有关于这些地址分类的研究,这使得它们的分类更具挑战性。在本文中,我们提出了一种基于机器学习(ML)的方法,用于对以太坊中的恶意地址进行分类,重点关注网络钓鱼、庞氏骗局和赌博地址。我们通过表格生成式对抗网络(GAN)使用选择性上采样技术来解决有限数据问题。我们使用以太坊交易数据对各种特征提取方法(包括 Trans2Vec 和 Node2Vec)进行了二元分类和多分类。我们根据 F1 分数、精确度、召回率和准确率对我们的方法进行了评估。结果表明,与最先进的方法相比,我们提出的方法在庞氏骗局和赌博检测方面非常有效。
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Leveraging tabular GANs for malicious address classification in ethereum network
The popularity of ethereum for cryptocurrency transactions attracts malicious actors to engage in illegal activities like phishing, ponzi, and gambling. Previous studies have focused mainly on phishing due to the large number of phishing addresses. However, there is no work done on ponzi or gambling classification due to the limited availability of these addresses, which makes their classification more challenging. In this paper, we propose a machine learning (ML) based method for classifying malicious addresses in ethereum, with a specific focus on phishing, ponzi, and gambling addresses. We use a selective upsampling technique through the tabular generative adversarial network (GAN) to solve limited data problems. We perform not only binary but also multiclass classification on various feature extraction methods, including Trans2Vec and Node2Vec, using Ethereum transactional data. We evaluate our method on F1 score, precision, recall, and accuracy. Our results show that the proposed method is effective in ponzi and gambling detection when compared with the state-of-the-art.
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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