Blockchain Security Analysis with Multi-Factor Authentication and Multi-Signature Mechanisms

Weifeng Li

2024

Abstract

Blockchain technology is renowned for its decentralization and transparency but faces significant security challenges, particularly in large-scale deployments where encryption and consensus mechanisms are vulnerable to attacks. This study aims to bolster blockchain security by employing advanced techniques, specifically focusing on multi-factor authentication (MFA) and multi-signature mechanisms. The research adopts a comprehensive approach that integrates Mythril's static code analysis, JUnit's dynamic testing, and Echidna's fuzz testing to identify and address vulnerabilities in smart contracts. Static code analysis is used to detect common vulnerabilities, dynamic testing ensures module functionality, and fuzz testing uncovers edge-case issues. The study demonstrates the effectiveness of MFA in mitigating risks associated with password leakage through static and one-time passwords, while the multi-signature mechanism enhances security by requiring multiple approvals for transactions. Experimental results on a publicly available smart contract dataset reveal that these security enhancements substantially reduce security incidents and improve system stability. These findings offer practical solutions for optimizing blockchain security and provide a solid foundation for future research on safeguarding blockchain applications in complex scenarios.

Paper Citation

in Harvard Style

Li W. (2024). Blockchain Security Analysis with Multi-Factor Authentication and Multi-Signature Mechanisms. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 390-394. DOI: 10.5220/0013524600004619

in Bibtex Style

@conference{daml24,
author={Weifeng Li},
title={Blockchain Security Analysis with Multi-Factor Authentication and Multi-Signature Mechanisms},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={390-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013524600004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Blockchain Security Analysis with Multi-Factor Authentication and Multi-Signature Mechanisms
SN - 978-989-758-754-2
AU - Li W.
PY - 2024
SP - 390
EP - 394
DO - 10.5220/0013524600004619
PB - SciTePress