基于深度神经网络的庞氏骗局合约检测方法

区块链技术的发展吸引了全球投资者的目光。目前,有数以万计的智能合约部署在以太坊上。在给金融、溯源等诸多行业带来颠覆性的创新之余,以太坊上的部分智能合约含有诸如庞氏骗局等欺诈形式,给全球投资者造成了数百万美元的损失。但是,目前针对互联网金融背景下庞氏骗局的定量识别方法较少,针对以太坊上庞氏骗局合约检测的研究较少,且检测精度有进一步提高的空间,文中提出基于深度神经网络的庞氏骗局合约检测方法。该方法提取出智能合约中有助于识别庞氏骗局的特征,如智能合约的操作码特征和账户特征,形成数据集,而后在数据集上训练模型,在测试集上检测性能。实验结果表明,基于深度神经网络的庞氏骗局合约检测方法具有99.6%的查准率和96.3%的查全率,均优于现有方法。

Deep Neural Network Based Ponzi Scheme Contract Detection Method

The development of blockchain technology has attracted the attention of global investors.Currently,tens of thousands of smart contracts are deployed on Ethereum.In spite of bringing disruptive innovation to finance,traceability and many other industries,some smart contracts on Ethereum contain fraudulent forms such as Ponzi schemes,causing millions of dollars of losses to global investors.However,at present,there are few quantitative identification methods for Ponzi scheme under the background of Internet finance,few researches on detection of Ponzi scheme contract on Ethereum,and the detection accuracy needs to be improved.Therefore,a Ponzi scheme contract detection method based on deep neural network is proposed.It extracts the features of smart contract that are helpful to identify Ponzi scheme,such as operation code features and account features,to form a data set.Then,the model is trained on the dataset and performance is tested on test set.The experimental results show that the Ponzi scheme contract detection method based on deep neural network has a precision of 99.6%and a recall rate of 96.3%,which are better than that of existing methods.