Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis
- URL: http://arxiv.org/abs/2407.18639v1
- Date: Fri, 26 Jul 2024 10:09:44 GMT
- Title: Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis
- Authors: Dalila Ressi, Alvise Spanò, Lorenzo Benetollo, Carla Piazza, Michele Bugliesi, Sabina Rossi,
- Abstract summary: We analyze the state of the art in machine-learning vulnerability detection for smart contracts.
We discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- Vulnerability Detection in Smart Contracts: A Comprehensive Survey [10.076412566428756]
This 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.
arXiv Detail & Related papers (2024-07-08T11:51:15Z) - Fixing Smart Contract Vulnerabilities: A Comparative Analysis of
Literature and Developer's Practices [6.09162202256218]
We refer to vulnerability fixing in the ways found in the literature as guidelines.
It is not clear to what extent developers adhere to these guidelines, nor whether there are other viable common solutions and what they are.
The goal of our research is to fill knowledge gaps related to developers' observance of existing guidelines and to propose new and viable solutions to security vulnerabilities.
arXiv Detail & Related papers (2024-03-12T09:55:54Z) - A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure.
This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus.
We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
arXiv Detail & Related papers (2024-01-19T14:52:04Z) - Vulnerability Scanners for Ethereum Smart Contracts: A Large-Scale Study [44.25093111430751]
In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars.
Various tools have been developed to detect and mitigate vulnerabilities in smart contracts.
This study investigates the gap between the effectiveness of existing security scanners and the vulnerabilities that still persist in practice.
arXiv Detail & Related papers (2023-12-27T11:26:26Z) - Survey on Quality Assurance of Smart Contracts [14.34073444030935]
With the increasing adoption of smart contracts, ensuring their security has become a critical concern.
We present a systematic overview of the quality assurance of smart contracts, covering vulnerabilities, attacks, defenses, and tool support.
In order to effectively protect smart contracts, we have created a labeled dataset to evaluate various vulnerability detection tools and compare their effectiveness.
arXiv Detail & Related papers (2023-11-01T03:36:24Z) - Designing an attack-defense game: how to increase robustness of
financial transaction models via a competition [69.08339915577206]
Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical.
We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input.
We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data.
The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
arXiv Detail & Related papers (2023-08-22T12:53:09Z) - An Automated Vulnerability Detection Framework for Smart Contracts [18.758795474791427]
We propose a framework to automatically detect vulnerabilities in smart contracts on the blockchain.
More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract.
Next, the collected vectors are fed into our novel metric learning-based deep neural network(DNN) to get the detection result.
arXiv Detail & Related papers (2023-01-20T23:16:04Z) - Pre-deployment Analysis of Smart Contracts -- A Survey [0.27195102129095]
We present a systematic review of the literature on smart contract vulnerabilities and methods.
Specifically, we enumerate and classify smart contract vulnerabilities and methods by the properties they address.
Several patterns about the strengths of different methods emerge through this classification process.
arXiv Detail & Related papers (2023-01-15T12:36:56Z) - Smart Contract Vulnerability Detection: From Pure Neural Network to
Interpretable Graph Feature and Expert Pattern Fusion [48.744359070088166]
Conventional smart contract vulnerability detection methods heavily rely on fixed expert rules.
Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge.
We develop automatic tools to extract expert patterns from the source code.
We then cast the code into a semantic graph to extract deep graph features.
arXiv Detail & Related papers (2021-06-17T07:12:13Z) - Inspect, Understand, Overcome: A Survey of Practical Methods for AI
Safety [54.478842696269304]
The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings.
In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged.
Our paper addresses both machine learning experts and safety engineers.
arXiv Detail & Related papers (2021-04-29T09:54:54Z) - ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep
Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable.
We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts.
We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.