# Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts

arXiv: https://arxiv.org/abs/2101.02377 · HTML: https://mlsanity.com/paper/2101.02377/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 59 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 24 of 24)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Learning Distributed Representations of Sentences from Unlabelled Data](https://mlsanity.com/paper/1602.03483/) (2016)
- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [Securify: Practical Security Analysis of Smart Contracts](https://mlsanity.com/paper/1806.01143/) (2018)
- [Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts](https://mlsanity.com/paper/1907.03890/) (2019)
- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [SAFE: Self-Attentive Function Embeddings for Binary Similarity](https://mlsanity.com/paper/1811.05296/) (2018)
- [Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs](https://mlsanity.com/paper/1808.04706/) (2018)
- [A Survey on Ethereum Systems Security: Vulnerabilities, Attacks and Defenses](https://mlsanity.com/paper/1908.04507/) (2019)
- [Finding The Greedy, Prodigal, and Suicidal Contracts at Scale](https://mlsanity.com/paper/1802.06038/) (2018)
- [Vandal: A Scalable Security Analysis Framework for Smart Contracts](https://mlsanity.com/paper/1809.03981/) (2018)
- [Sereum: Protecting Existing Smart Contracts Against Re-Entrancy Attacks](https://mlsanity.com/paper/1812.05934/) (2018)
- [A Semantic Framework for the Security Analysis of Ethereum smart contracts](https://mlsanity.com/paper/1802.08660/) (2018)
- [The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts](https://mlsanity.com/paper/1902.06976/) (2019)
- [eThor: Practical and Provably Sound Static Analysis of Ethereum Smart Contracts](https://mlsanity.com/paper/2005.06227/) (2020)
- [Checking Smart Contracts with Structural Code Embedding](https://mlsanity.com/paper/2001.07125/) (2020)
- [Under-Optimized Smart Contracts Devour Your Money](https://mlsanity.com/paper/1703.03994/) (2017)
- [walk2friends: Inferring Social Links from Mobility Profiles](https://mlsanity.com/paper/1708.08221/) (2017)
- [ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks](https://mlsanity.com/paper/1905.12590/) (2019)
- [Annotary: A Concolic Execution System for Developing Secure Smart Contracts](https://mlsanity.com/paper/1907.03868/) (2019)
- [AEGIS: Shielding Vulnerable Smart Contracts Against Attacks](https://mlsanity.com/paper/2003.05987/) (2020)
- [Hunting for Re-Entrancy Attacks in Ethereum Smart Contracts via Static Analysis](https://mlsanity.com/paper/2007.01029/) (2020)

## Built on by (most cited first, 7 of 7)

- [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024)
- [SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework](https://mlsanity.com/paper/2411.19234/) (2024)
- [Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis](https://mlsanity.com/paper/2407.18639/) (2024)
- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [Dual-view Aware Smart Contract Vulnerability Detection for Ethereum](https://mlsanity.com/paper/2407.00336/) (2024)
- [SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts](https://mlsanity.com/paper/2509.05681/) (2025)
- [Detection and Analysis of Sensitive and Illegal Content on the Ethereum Blockchain Using Machine Learning Techniques](https://mlsanity.com/paper/2512.17411/) (2025)

## Abstract (arXiv)

Ethereum smart contracts are programs that run on the Ethereum blockchain, and many smart contract vulnerabilities have been discovered in the past decade. Many security analysis tools have been created to detect such vulnerabilities, but their performance decreases drastically when codes to be analyzed are being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool for vulnerability detection, with robustness against code rewrites in smart contracts. Existing machine-learning-based static analysis tools for vulnerability detection need features, which analysts create manually, as inputs. In contrast, Eth2Vec automatically learns features of vulnerable Ethereum Virtual Machine (EVM) bytecodes with tacit knowledge through a neural network for language processing. Therefore, Eth2Vec can detect vulnerabilities in smart contracts by comparing the code similarity between target EVM bytecodes and the EVM bytecodes it already learned. We conducted experiments with existing open databases, such as Etherscan, and our results show that Eth2Vec outperforms the existing work in terms of well-known metrics, i.e., precision, recall, and F1-score. Moreover, Eth2Vec can detect vulnerabilities even in rewritten codes.