Detecting Ethereum Smart Contract Vulnerabilities via Bytecode Image Analysis

Abstract

Smart contracts are increasingly being adopted in modern supply chain (SC) systems, offering a transformative shift from traditional centralized models to decentralized, automated, and trustless processes. However, vulnerabilities in the chain of smart contract represent a critical threat to the reliability and security of supply chain ecosystems, often arising from intricate logical flaws, unintended inter-contract interactions, or improper handling of user input—issues that remain difficult to uncover through conventional testing or manual auditing. This paper introduces an innovative deep learning–based methodology that transforms smart contract bytecode into image representations, enabling precise and efficient classification of diverse vulnerability patterns. Unlike existing approaches that rely on source code availability or dynamic execution, the proposed framework operates independently of source code and circumvents the limitations inherent to dynamic analysis, thereby offering a versatile and system-agnostic solution. Experimental evaluations conducted on a newly curated dataset collected from multiple publicly available repositories demonstrate the robustness of the proposed method, achieving 92.24% accuracy and an 89.06% F1-score. Beyond its strong empirical performance, the framework ensures reproducibility, data transparency, and adaptability across heterogeneous blockchain environments. Collectively, these contributions establish a comprehensive and accessible foundation for enhancing the detection, mitigation, and overall resilience of blockchain smart contracts.