ChainSniper: A Machine Learning Approach for Auditing Cross-Chain Smart Contracts

Abstract

Smart contracts are autonomous programs stored on blockchain networks that self-execute agreed terms in a transparent and accurate manner. Within cross-chain platforms, smart contracts facilitate interaction and exchange of data between diverse blockchains. However, the presence of vulnerabilities in smart contracts renders them susceptible to exploitation, jeopardizing security. Considerable research has focused on identifying and detecting such vulnerabilities, though existing approaches have yet to achieve comprehensive coverage. This paper presents ChainSniper, a sidechain-based framework integrating machine learning to automatically appraise vulnerabilities in cross-chain smart contracts. A comprehensive dataset, denoted "CrossChainSentinel", was compiled comprising 300 manually labeled code snippets. This dataset was leveraged to train machine learning models discerning vulnerable versus secure smart contracts. Experimental findings demonstrate the viability of machine learning methodologies for enhancing smart contract auditing within decentralized applications spanning multiple networks. Notable detection precision was achieved, substantiating ChainSniper’s potential to strengthen security analysis through an automated and expansive evaluation of smart contract code.