Authors:
Ali Nikhalat-Jahromi
1
;
Ali Saghiri
1
;
2
and
Mohammad Meybodi
1
Affiliations:
1
Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran
;
2
Department of Computer Science, William Paterson University, New Jersey, U.S.A.
Keyword(s):
Blockchain, Proof-of-Work, Selfish Mining, Defense Mechanism, Reinforcement Learning, Q-Learning.
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 modi
fication 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.
(More)