Taxonomic Insights into Ethereum Smart Contracts: Linking Application Categories to Security Vulnerabilities

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Introduction

Ethereum, launched in 2015, expanded the capabilities of blockchain technology with its Turing-complete smart contracts and the Ethereum Virtual Machine (EVM). Unlike Bitcoin’s limited smart contract functionality, Ethereum enabled complex decentralized applications (dApps), fostering a vibrant developer ecosystem.

Smart contracts—self-executing agreements coded on blockchain—have revolutionized dApp creation. However, their rapid proliferation (over 100,000 contracts analyzed in this study) introduces challenges:

Research Objectives

This study aims to:

  1. Develop a taxonomy of Ethereum smart contracts.
  2. Analyze their evolution over time.
  3. Link categories to specific vulnerabilities.

Key Contributions:


Related Work

Smart Contract Classification

Vulnerability Detection

Gaps Addressed:


Methodology

Dataset

Topic Modeling (Seeded LDA)

  1. Preprocessing:

    • Tokenized Solidity code, filtered programming keywords.
    • Handled camelCase/snake_case conventions.
  2. Model Configuration:

    • 15 topics via coherence scores.
    • Seed terms (e.g., "lock," "bid," "NFT") guided topic discovery.

Vulnerability Tools


Results

Smart Contract Taxonomy

| Category | Example Keywords | Use Case |
|----------------------------|-----------------------------|-----------------------------|
| Token | burn, exchange, ERC20 | Cryptocurrency creation |
| Certification & NFT | authenticate, ownership | Digital asset verification |
| Gambling | bet, dice, prize | Decentralized casinos |
| Bank | deposit, withdraw | Ether storage |

Macro-Categories:

Temporal Trends

Vulnerability Correlations

| Category | Top Vulnerability | Chi-Square Contribution |
|---------------------|-----------------------------|-----------------------------|
| Gambling | Bad Randomness (BR) | 25.15% |
| Certification/NFT | Concurrency (C) | 12.32% |


Discussion

Limitations:


Conclusion

This study bridges smart contract categorization and security analysis, offering a framework for risk assessment. Future work:

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FAQ

Q1: What are the most common smart contract categories?
A1: Tokens (40%), NFTs (25%), and gambling (15%) dominate Ethereum.

Q2: How do vulnerabilities vary by category?
A2: E.g., gambling → bad randomness; NFTs → concurrency risks.

Q3: Why use seeded LDA?
A3: Seed terms improve accuracy for domain-specific terms (e.g., "burn" in tokens).

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