Q-Defense: When Q-Learning Comes to Help Proof-of-Work Against the Selfish Mining Attack

Ali Nikhalat-Jahromi, Ali Saghiri, Ali Saghiri, Mohammad Meybodi

2024

Abstract

The Proof-of-Work (PoW) consensus protocol is widely utilized in various blockchain implementations, including Bitcoin. The security of this protocol relies heavily on the incentive-compatibility of participating miner, who compete against each other to discover new blocks. However, the assumption that competition will naturally evolve into collaboration, ensuring blockchain security, is not always valid. Certain colluding miners, known as ”selfish miners ,” attempt to unfairly obtain rewards by deviating from the prescribed protocol. In this paper, we propose a novel learning-based mechanism to address this challenge and enhance the PoW protocol. Specifically, we apply Q-Learning, a prominent technique in reinforcement learning, to each miner in order to mitigate the impact of selfish collaboration among colluding miners. To best of our knowledge, this is the first defense mechanism based on Q-Learning in the literature. Our comprehensive analysis demonstrates that the proposed modification to the PoW protocol can increase the threshold for successful selfish mining attacks from 25% to 40%. Furthermore, simulation results comparing our defense mechanism with tie-breaking, a well-known defense approach, validate the effectiveness of our proposed mechanism.

Paper Citation

in Harvard Style

Nikhalat-Jahromi A., Saghiri A. and Meybodi M. (2024). Q-Defense: When Q-Learning Comes to Help Proof-of-Work Against the Selfish Mining Attack. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 37-46. DOI: 10.5220/0012378600003636

in Bibtex Style

@conference{icaart24,
author={Ali Nikhalat-Jahromi and Ali Saghiri and Mohammad Meybodi},
title={Q-Defense: When Q-Learning Comes to Help Proof-of-Work Against the Selfish Mining Attack},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={37-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012378600003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Q-Defense: When Q-Learning Comes to Help Proof-of-Work Against the Selfish Mining Attack
SN - 978-989-758-680-4
AU - Nikhalat-Jahromi A.
AU - Saghiri A.
AU - Meybodi M.
PY - 2024
SP - 37
EP - 46
DO - 10.5220/0012378600003636
PB - SciTePress