Smart contract security detection method and system, equipment, terminal and application

Abstract

The invention belongs to the technical field of block chain security, and discloses a smart contract security detection method and system, equipment, a terminal and application, the smart contract security detection method comprises the following steps: using an open source code to train a word2vec model; packaging the open source code into an intelligent contract function according to an intelligent contract grammar; converting the packaged function into an abstract syntax tree, and extracting data flow and control flow information; converting data flow and control flow information of the smart contract into a graph model; using a trained word2vec model to convert the graph nodes into vectors; training the graph model by using a graph neural network; reading all node information, and converting the intelligent contract function graph model into vectors; and judging whether the function vector contains the intelligent contract vulnerability information or not by using the classification model. According to the invention, the security detection efficiency of the smart contract is improved, and a good effect is achieved.

Description

technical field [0001] The invention belongs to the technical field of block chain security, and in particular relates to a smart contract security detection method, system, equipment, terminal and application. Background technique [0002] At present, most of the smart contracts on the blockchain platform involve the transaction and processing of digital assets or cryptocurrencies, so the loopholes in smart contracts may be exploited to expose users to malicious attacks. For this reason, in recent years, more and more researchers have begun to study the security detection methods for smart contracts, but the existing research is mainly carried out around the Ethereum platform, and the smart contracts of the alliance chain platform represented by Hyperledger Fabric However, there is a lack of targeted detection methods for security detection. The main reasons are as follows: [0003] (1) Smart contracts on the Fabric platform are mainly deployed within the organization and...

AI Technical Summary

Problems solved by technology

[0003] (1) Smart contracts on the Fabric platform are mainly deployed within the organization and are difficult to obtain;

[0004] (2) Ethereum and Fabric have different platform characteristics, and the security detection tools and methods of the two platforms cannot be directly interoperable;

[0005] (3) There are few smart contracts publicly available on the Fabric platform, and it is impossible to conduct large-scale analysis and research

[0008] (1) The existing research is mainly carried out around the Ethereum platform, but there is a lack of targeted detection methods for the smart contract security detection of the alliance chain platform represented by Hyperledger Fabric

[0009] (2) Smart contracts on the Fabric platform are mainly deployed within the organization and are difficult to obtain; there are few smart contracts on the Fabric platform, and large-scale analysis and research cannot be carried out

[0010] (3) Ethereum and Fabric have different platform characteristics, and the security detection tools and methods of the two platforms cannot be directly interoperable

[0012] On the one hand, due to the lack of smart contract codes disclosed on the Fabric platform, for the graph neural network, the available data sets are insufficient, and the lack of training data limits the quality of the machine learning detection model, which increases the difficulty of model training in the present invention; On the one hand, the existing blockchain platform contract security detection tools are mainly aimed at the Ethereum platform, which cannot be directly used on the Fabric platform, and there is an extreme lack of information on Fabric contract vulnerability detection for reference, which further increases the difficulty of implementing the solution

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