Taxonomic insights into ethereum smart contracts by linking application categories to security vulnerabilities

Abstract

Abstract The expansion of smart contracts on the Ethereum blockchain has created a diverse ecosystem of decentralized applications. This growth, however, poses challenges in classifying and securing these contracts. Existing research often separately addresses either classification or vulnerability detection, without a comprehensive analysis of how contract types are related to security risks. Our study addresses this gap by developing a taxonomy of smart contracts and examining the potential vulnerabilities associated with each category. We use the Latent Dirichlet Allocation (LDA) model to analyze a dataset of over 100,040 Ethereum smart contracts, which is notably larger than those used in previous studies. Our analysis categorizes these contracts into eleven groups, with five primary categories: Notary, Token, Game, Financial, and Blockchain interaction. This categorization sheds light on the various functions and applications of smart contracts in today’s blockchain environment. In response to the growing need for better security in smart contract development, we also investigate the link between these categories and common vulnerabilities. Our results identify specific vulnerabilities associated with different contract types, providing valuable insights for developers and auditors. This relationship between contract categories and vulnerabilities is a new contribution to the field, as it has not been thoroughly explored in previous research. Our findings offer a detailed taxonomy of smart contracts and practical recommendations for enhancing security. By understanding how contract categories correlate with vulnerabilities, developers can implement more effective security measures, and auditors can better prioritize their reviews. This study advances both academic knowledge of smart contracts and practical strategies for securing decentralized applications on the Ethereum platform.