【Author】
Ostapowicz, Michal; Zbikowski, Kamil
【Source】WEB INFORMATION SYSTEMS ENGINEERING - WISE 2019
【Abstract】Applications of blockchain technologies got a lot of attention in recent years. They exceed beyond exchanging value and being a substitute for fiat money and traditional banking system. Nevertheless, being able to exchange value on a blockchain is at the core of the entire system and has to be reliable. Blockchains have built-in mechanisms that guarantee whole system's consistency and reliability. However, malicious actors can still try to steal money by applying well known techniques like malware software or fake emails. In this paper we apply supervised learning techniques to detect fraudulent accounts on Ethereum blockchain. We compare capabilities of Random Forests, Support Vector Machines and XGBoost classifiers to identify such accounts basing on a dataset of more than 300 thousands accounts. Results show that we are able to achieve recall and precision values allowing for the designed system to be applicable as an anti-fraud rule for digital wallets or currency exchanges. We also present sensitivity analysis to show how presented models depend on particular feature and how lack of some of them will affect the overall system performance.
【Keywords】Blockchain; Anti-fraud; Supervised; Xgboost; Random forests; SVM; Ethereum
【摘要】区块链技术的应用近年来受到了广泛关注。它们超越了价值交换，成为法定货币和传统银行体系的替代品。然而，能够在区块链上交换价值是整个系统的核心，而且必须是可靠的。区块链有内置的机制来保证整个系统的一致性和可靠性。然而，恶意行为者仍然可以通过使用恶意软件或伪造电子邮件等众所周知的技术来窃取金钱。在本文中，我们应用监督学习技术来检测以太坊区块链上的欺诈账户。我们比较了随机森林、支持向量机和XGBoost分类器的能力，以基于超过30万个账户的数据集识别此类账户。结果表明，我们能够实现召回和精度值，允许设计的系统适用于数字钱包或货币交易所的反欺诈规则。我们还将提供敏感性分析，以显示所呈现的模型如何依赖于特定的特性，以及缺少某些特性将如何影响整体系统性能。
【关键词】区块链;反欺诈;监督;Xgboost;随机森林;支持向量机;以太坊
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