Application of artificial intelligence to optimize smart contracts in blockchain systems using graph theory

Abstract

This article examines the application of artificial intelligence methods to optimize smart contracts in blockchain systems using graph theory. The introduction outlines the current problems of traditional smart contract analysis, including the limitations of existing static and dynamic analysis methods, as well as the growing complexity and vulnerability of blockchain applications in the context of the rapid development of cyber threats. The necessity of integrating machine learning algorithms with graph theory methods to increase the reliability and efficiency of smart contracts is substantiated. The methodological part of the study describes a developed model that uses artificial intelligence algorithms to analyze the structural features of smart contracts represented as graphs. The stages of graph model construction are described in detail: from data preprocessing and the formation of nodal representations to the application of clustering algorithms and anomaly search. The work uses a combined approach to identify hidden relationships between the components of the blockchain system, as well as optimize the processes of verification and contract execution. The experimental results demonstrate that the proposed model significantly improves the accuracy of vulnerability detection and optimization efficiency compared to traditional methods. In the course of comparing performance and security, the model showed an improvement in key indicators by 25-30%, which is confirmed by both statistical and practical tests on real data. The analysis of graph structures has revealed previously unnoticed patterns of interaction between the elements of smart contracts, which opens up new opportunities for their automated audit. The discussion of the results focuses on the advantages of integrating artificial intelligence and graph theory while ensuring the security of blockchain systems, and provides recommendations for further improving the methodology, taking into account the specifics of various blockchain platforms. The limitations of the study related to the need for high-quality source data and scaling algorithms to distributed networks are noted. In conclusion, it is concluded that the developed model is important for increasing the stability and functionality of smart contracts, which allows us to count on its further adaptation in related areas of digital security and the fintech industry.