Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914063044313088 |
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| author | Ressi, Dalila Spanò, Alvise Benetollo, Lorenzo Piazza, Carla Bugliesi, Michele Rossi, Sabina |
| author_facet | Ressi, Dalila Spanò, Alvise Benetollo, Lorenzo Piazza, Carla Bugliesi, Michele Rossi, Sabina |
| contents | Smart contracts are central to a myriad of critical blockchain applications, from financial transactions to supply chain management. However, their adoption is hindered by security vulnerabilities that can result in significant financial losses. Most vulnerability detection tools and methods available nowadays leverage either static analysis methods or machine learning. Unfortunately, as valuable as they are, both approaches suffer from limitations that make them only partially effective. In this survey, we analyze the state of the art in machine-learning vulnerability detection for Ethereum smart contracts, by categorizing existing tools and methodologies, evaluating them, and highlighting their limitations. Our critical assessment unveils issues such as restricted vulnerability coverage and dataset construction flaws, providing us with new metrics to overcome the difficulties that restrain a sound comparison of existing solutions. Driven by our findings, we discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts. Our guidelines address the known flaws while at the same time opening new avenues for research and development. By shedding light on current challenges and offering novel directions for improvement, we contribute to the advancement of secure smart contract development and blockchain technology as a whole. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18639 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis Ressi, Dalila Spanò, Alvise Benetollo, Lorenzo Piazza, Carla Bugliesi, Michele Rossi, Sabina Cryptography and Security Machine Learning Smart contracts are central to a myriad of critical blockchain applications, from financial transactions to supply chain management. However, their adoption is hindered by security vulnerabilities that can result in significant financial losses. Most vulnerability detection tools and methods available nowadays leverage either static analysis methods or machine learning. Unfortunately, as valuable as they are, both approaches suffer from limitations that make them only partially effective. In this survey, we analyze the state of the art in machine-learning vulnerability detection for Ethereum smart contracts, by categorizing existing tools and methodologies, evaluating them, and highlighting their limitations. Our critical assessment unveils issues such as restricted vulnerability coverage and dataset construction flaws, providing us with new metrics to overcome the difficulties that restrain a sound comparison of existing solutions. Driven by our findings, we discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts. Our guidelines address the known flaws while at the same time opening new avenues for research and development. By shedding light on current challenges and offering novel directions for improvement, we contribute to the advancement of secure smart contract development and blockchain technology as a whole. |
| title | Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2407.18639 |