论文标题
使用图神经网络对以太坊的网络钓鱼欺诈检测
Phishing Fraud Detection on Ethereum using Graph Neural Network
论文作者
论文摘要
区块链在金融领域具有广泛的应用程序，但也吸引了网络犯罪的增加。最近，网络钓鱼欺诈已成为对区块链安全的主要威胁，呼吁制定有效的监管策略。如今，网络科学已被广泛用于建模以太坊交易数据，进一步引入了网络表示学习技术以分析交易模式。在本文中，我们将网络钓鱼检测视为一项图形分类任务，并提出了端到端网络钓鱼检测图神经网络框架（PDGNN）。具体而言，我们首先构建一个轻巧的以太坊交易网络，并提取收集的网络钓鱼帐户的交易子图。然后，我们提出了一个基于Chebyshev-GCN的端到端检测模型，以精确区分正常帐户和网络钓鱼帐户。在五个以太坊数据集上进行的广泛实验表明，我们的PDGNN在大型交易网络中均明显超过一般的网络钓鱼检测方法和尺度。
Blockchain has widespread applications in the financial field but has also attracted increasing cybercrimes. Recently, phishing fraud has emerged as a major threat to blockchain security, calling for the development of effective regulatory strategies. Nowadays network science has been widely used in modeling Ethereum transaction data, further introducing the network representation learning technology to analyze the transaction patterns. In this paper, we consider phishing detection as a graph classification task and propose an end-to-end Phishing Detection Graph Neural Network framework (PDGNN). Specifically, we first construct a lightweight Ethereum transaction network and extract transaction subgraphs of collected phishing accounts. Then we propose an end-to-end detection model based on Chebyshev-GCN to precisely distinguish between normal and phishing accounts. Extensive experiments on five Ethereum datasets demonstrate that our PDGNN significantly outperforms general phishing detection methods and scales well in large transaction networks.
