Mitigating the Vector76 Attack: Enhancing Security in the Bitcoin Network

Haoyun Tang

2024

Abstract

This research paper presents a comprehensive analysis of the Vector76 attack within the Bitcoin network, a notable double-spending threat that undermines the integrity of blockchain transactions. Similar to the Finney attack, the Vector76 attack exploits Bitcoin's pre-mining function, enabling miners to secretly mine and strategically broadcast blocks, thus deceiving the network into accepting fraudulent transactions. The study investigates the operational principles of the Vector76 attack, its generalized versions, and the influence of selfish mining in facilitating these attacks. Furthermore, this research outlines a series of countermeasures, including security strategies designed to thwart double-spending attacks and monitoring technologies such as Enhanced Observer (ENHOBS), which are employed to review transactions and detect anomalies. Additionally, the study examines detection and punitive mechanisms aimed at combating selfish mining attacks, thus safeguarding the blockchain from malicious mining behaviors. In conclusion, a thorough review of the findings highlights the critical need for robust defense mechanisms to protect the Bitcoin ecosystem against complex threats. The implications of this research extend to the wider cryptocurrency community, underscoring the necessity for ongoing innovation in security strategies to address emerging vulnerabilities.

Paper Citation

in Harvard Style

Tang H. (2024). Mitigating the Vector76 Attack: Enhancing Security in the Bitcoin Network. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 500-505. DOI: 10.5220/0013527100004619

in Bibtex Style

@conference{daml24,
author={Haoyun Tang},
title={Mitigating the Vector76 Attack: Enhancing Security in the Bitcoin Network},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={500-505},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013527100004619},
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 - Mitigating the Vector76 Attack: Enhancing Security in the Bitcoin Network
SN - 978-989-758-754-2
AU - Tang H.
PY - 2024
SP - 500
EP - 505
DO - 10.5220/0013527100004619
PB - SciTePress