文献类型：期刊文献

英文题名：Smart Contract Classification With a Bi-LSTM Based Approach

作者：Tian, Gang[1];Wang, Qibo[1];Zhao, Yi[2];Guo, Lantian[3];Sun, Zhonglin[1];Lv, Liangyu[1]

机构：[1]Shangdong Univ Sci & Technol, Sch Comp Sci & Engn, Qingdao 266590, Peoples R China;[2]Guangdong Ocean Univ, Sch Math & Comp Sci, Zhanjiang 524088, Peoples R China;[3]Qingdao Univ Sci & Technol, Sch Automat & Elect Engn, Qingdao 266044, Peoples R China

年份：2020

卷号：8

起止页码：43806

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000524710900036)、、EI(收录号：20201308341627)、Scopus(收录号：2-s2.0-85082022978)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61702305, Grant 11971270, and Grant 61903089, in part by the China Postdoctoral Science Foundation under Grant 2017M622234, and in part by the Science and Technology Support Plan of Youth Innovation Team of Shandong higher School under Grant 2019KJN2014.

语种：英文

外文关键词：Smart contracts; Semantics; Feature extraction; Context modeling; Blockchain; Data models; Smart contract classification; Bi-LSTM; attention mechanism; Gaussian LDA; account information

外文摘要：With the number of smart contracts growing rapidly, retrieving the relevant smart contracts quickly and accurately has become an important issue. A key step for recognizing the related smart contracts is able to classify them accurately. Different from traditional text, the smart contract is composed of several parts: source code, code comments and other useful information like account information. How to make good use of those different kinds of features for effective classification is a problem need to be solved. Inspired by this, we proposed a smart contract classification approach based on Bi-LSTM model and Gaussian LDA, which can use a variety of information as inputs of the model, including source code, comments, tags, account and other content information. Bi-LSTM is utilized to capture grammar rules and context information in source code, while Gaussian LDA model is employed to generate comments feature where the semantics of the comments are enriched by embeddings. We also use attention mechanism to focus on the more relevant features in smart contracts for tags and fuse account information to provide additional information for classification. The experimental results show that the classification performance of the proposed model is superior to other baseline models.