AI-Driven Opcode-Based Smart Contract Classification

Abstract

In the ever-expanding landscape of blockchain technology and decentralized applications (DApps), smart contracts play a pivotal role in driving blockchain functionality and shaping interactions and transactions. This paper introduces an AI-driven approach to classify smart contracts based on their opcode patterns, culminating in the identification of three distinct classes: Primary Smart Contracts (SC-P), representing fundamental blockchain interactions; Oracle Smart Contracts (SC-O), facilitating real-world data integration; and Bridge Smart Contracts (SC-B), enabling seamless communication across blockchains. Our methodology employs various advanced AI models, including Random Forest, Support Vector Machine, Convolutional Neural Network, and autoencoder. These models are rigorously trained and evaluated using a diverse dataset comprising real Ethereum smart contracts, providing valuable insights into the characteristics and functionalities of each class. This work not only advances our comprehension of smart contract functionalities but also establishes a robust classification paradigm for resource allocation and communication patterns within blockchain ecosystems, thereby contributing to the broader field of blockchain research.