Machine Learning-Based Intelligent Contract Invocation Security Evaluation Technology

In the early 21 st century,blockchain technology,as a revolutionary distributed ledger technology,has attracted widespread attention from academia and industry.Smart contracts are considered one of the core innovations of blockchain technology.However,the security issues of smart contracts have gradually been exposed,posing various risks to users and systems.To address this issue,this paper proposes two techniques and a system,including a smart contract reputation evaluation technique based on historical behavior analysis,a call risk assessment technique based on reputation evaluation,and a smart contract security evaluation system.First,this study proposes a credibility evaluation technique based on the analysis of the historical behavior of smart contracts.For the first time,this study incorporates the behavior features of smart contracts and their calling relationships in the historical behavior of smart contracts into the research scope.By setting a series of indicators and using Graph Convolutional Networks(GCN)method,this study realizes the security evaluation of smart contracts and their calling relationships,and derives the credibility evaluation of smart contracts accordingly.This evaluation method helps to identify potential security risks and provides a strong guarantee for the security of smart contracts.To validate the effectiveness of the proposed method,this study adopts K-fold cross-validation and sets up a control group,which separately adopts smart contract credibility evaluation techniques based on traditional machine learning algorithms,such as decision trees,support vector machines,and random forests.By comparing the evaluation indicators of the experimental group and the control group,this study can intuitively evaluate the advantages of the method based on graph convolutional networks in the field of smart contract credibility evaluation.Secondly,this study presents a call risk assessment technique based on reputation evaluation technology.This technique establishes an evaluation model that comprehensively considers factors such as smart contract reputation evaluation,call depth,call frequency,fund flow,and developer reputation,to achieve real-time assessment of smart contract call risk.Based on this,the study classifies call risk levels and validates the assessment methods through K-fold cross-validation and various evaluation indicators.At the same time,in conjunction with the reputation evaluation results,a call risk assessment method is proposed,which realizes early warning and prevention of smart contract call risks.Finally,this paper designs and implements a smart contract security evaluation system.The system integrates the aforementioned two techniques,providing a convenient and easy-to-use smart contract security evaluation tool.Through practical application verification,this paper demonstrates the effectiveness and practicality of the system in smart contract security evaluation.The research results of this paper will contribute to improving the security and reliability of smart contracts,providing technical support for the development of the blockchain industry.Through this research,we hope to make the application of smart contracts in finance,the Internet of Things,supply chain management,and other fields more secure,further promoting the development of blockchain technology.