【Author】
Yuan, Qi; Huang, Baoying; Zhang, Jie; Wu, Jiajing; Zhang, Haonan; Zhang, Xi
【Source】2020 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
【Abstract】With the increasing popularity of blockchain technology, it has also become a hotbed of various cybercrimes. As a traditional way of scam, the phishing scam has new means of scam in the blockchain scenario and swindles a lot of money from users. In order to create a safe environment for investors, an efficient method for phishing detection is urgently needed. In this paper, we propose a three steps framework to detect phishing scams on Ethereum by mining Ethereum transaction records. First, we obtain the labeled phishing accounts and corresponding transaction records from two authorized websites. According to the collected transaction records we build an Ethereum transaction network. Then, a network embedding method node2vec which can extract the latent features of accounts is used for subsequent phishing classification. Finally, to distinguish whether the account is a phishing account, we adopt the one class support vector machine (SVM) to classify. The experimental result demonstrates that F-score of our phishing detection method can achieve 0.846, which verifies the validity of our model. To the best of our knowledge, this is the first work that investigates the phishing scams on Ethereum based on transaction records.
【摘要】随着区块链技术的日益普及，它也成为各种网络犯罪的温床。网络钓鱼诈骗作为一种传统的诈骗方式，在区块链场景中有了新的诈骗手段，从用户那里骗取了大量的金钱。为了给投资者创造一个安全的环境，迫切需要一种有效的网络钓鱼检测方法。在本文中，我们提出了一个三步框架，通过挖掘以太坊交易记录来检测以太坊上的钓鱼诈骗。首先，我们从两个授权网站获取标注的钓鱼账户和相应的交易记录。根据收集到的交易记录，我们构建了以太坊交易网络。然后，利用能够提取账户潜在特征的网络嵌入方法node2vec进行后续的钓鱼分类。最后，为了区分账户是否为钓鱼账户，我们采用一类支持向量机(SVM)进行分类。实验结果表明，我们的网络钓鱼检测方法的f值可以达到0.846，验证了我们模型的有效性。据我们所知，这是基于交易记录调查以太坊网络钓鱼诈骗的第一项工作。
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