Publication detail

Detecting Smart Contract Vulnerabilities with Combined Binary and Multiclass Classification

MEZINA, A. OMETOV, A.

Original Title

Detecting Smart Contract Vulnerabilities with Combined Binary and Multiclass Classification

Type

journal article in Web of Science

Language

English

Original Abstract

The development of Distributed Ledger Technology (DLT) is pushing toward automating decentralized data exchange processes. One of the key components of this evolutionary step is facilitating smart contracts that, in turn, come with several additional vulnerabilities. Despite the existing tools for analyzing smart contracts, keeping these systems running and preserving performance while maintaining a decent level of security in a constantly increasing number of contracts becomes challenging. Machine Learning (ML) methods could be utilized for analyzing and detecting vulnerabilities in DLTs. This work proposes a new ML-based two-phase approach for the detection and classification of vulnerabilities in smart contracts. Firstly, the system’s operation is set up to filter the valid contracts. Secondly, it focuses on detecting a vulnerability type, if any. In contrast to existing approaches in this field of research, our algorithm is more focused on vulnerable contracts, which allows to save time and computing resources in the production environment. According to the results, it is possible to detect vulnerability types with an accuracy of 0.9921, F1 score of 0.9902, precision of 0.9883, and recall of 0.9921 within reasonable execution time, which could be suitable for integrating existing DLTs.

Keywords

modeling; classification; vulnerability detection; distributed systems

Authors

MEZINA, A.; OMETOV, A.

Released

7. 7. 2023

Publisher

MDPI

Location

SWITZERLAND

ISBN

2410-387X

Periodical

Cryptography

Year of study

7

Number

3

State

Swiss Confederation

Pages from

1

Pages to

20

Pages count

20

URL

https://www.mdpi.com/2410-387X/7/3/34

Full text in the Digital Library

http://hdl.handle.net/11012/213591

BibTex

@article{BUT183983,
  author="Anzhelika {Mezina} and Aleksandr {Ometov}",
  title="Detecting Smart Contract Vulnerabilities with Combined Binary and Multiclass Classification",
  journal="Cryptography",
  year="2023",
  volume="7",
  number="3",
  pages="1--20",
  doi="10.3390/cryptography7030034",
  issn="2410-387X",
  url="https://www.mdpi.com/2410-387X/7/3/34"
}