# Vulnerability Detection in Smart Contracts: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2407.07922 · HTML: https://mlsanity.com/paper/2407.07922/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 126 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion](https://mlsanity.com/paper/2106.09282/) (2021)
- [An Overview on Smart Contracts: Challenges, Advances and Platforms](https://mlsanity.com/paper/1912.10370/) (2019)
- [Finding The Greedy, Prodigal, and Suicidal Contracts at Scale](https://mlsanity.com/paper/1802.06038/) (2018)
- [Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing](https://mlsanity.com/paper/2004.00362/) (2020)
- [Dynamic Vulnerability Detection on Smart Contracts Using Machine Learning](https://mlsanity.com/paper/2102.07420/) (2021)
- [ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning](https://mlsanity.com/paper/2103.12607/) (2021)
- [xFuzz: Machine Learning Guided Cross-Contract Fuzzing](https://mlsanity.com/paper/2111.12423/) (2021)
- [MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities](https://mlsanity.com/paper/2208.13252/) (2022)
- [VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model](https://mlsanity.com/paper/2309.08474/) (2023)
- [Hunting the Ethereum Smart Contract: Color-inspired Inspection of Potential Attacks](https://mlsanity.com/paper/1807.01868/) (2018)
- [Evolution of Automated Weakness Detection in Ethereum Bytecode: a Comprehensive Study](https://mlsanity.com/paper/2303.10517/) (2023)
- [Vulnerable Smart Contract Function Locating Based on Multi-Relational Nested Graph Convolutional Network](https://mlsanity.com/paper/2306.04479/) (2023)

## Abstract (arXiv)

In the growing field of blockchain technology, smart contracts exist as transformative digital agreements that execute transactions autonomously in decentralised networks. However, these contracts face challenges in the form of security vulnerabilities, posing significant financial and operational risks. While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures. We endeavour to fill an important gap in the existing literature by conducting a rigorous systematic review, exploring the intersection between machine learning and smart contracts. Specifically, the study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts. We analysed 88 articles published between 2018 and 2023 from the following databases: IEEE, ACM, ScienceDirect, Scopus, and Google Scholar. The findings reveal that classical machine learning techniques, including KNN, RF, DT, XG-Boost, and SVM, outperform static tools in vulnerability detection. Moreover, multi-model approaches integrating deep learning and classical machine learning show significant improvements in precision and recall, while hybrid models employing various techniques achieve near-perfect performance in vulnerability detection accuracy. By integrating state-of-the-art solutions, this work synthesises current methods, thoroughly investigates research gaps, and suggests directions for future studies. The insights gathered from this study are intended to serve as a seminal reference for academics, industry experts, and bodies interested in leveraging machine learning to enhance smart contract security.