Author ORCID Identifier
https://orcid.org/0000-0002-7354-6210
Date of Award
17-8-2025
Document Type
Thesis
School
School of Computing
Programme
Ph.D.-Doctoral of Philosophy
First Advisor
Dr.K.Geetha
Keywords
Selfish Mining, Proof of Work, Mining Pools, Honest Mining, Crypto Address, Q Learning, Upper confidence bound algorithm
Abstract
Blockchain, an innovative decentralized distributed, disrupting programming paradigm embodies key principles such as decentralization, data provenance, immutability, and transparency. At its core blockchain begins with the genesis block and progresses with each subsequent block containing the hash of the previous block, Merkle root, timestamp, a coin base transaction address, and a nonce. Miners compete to discover a target hash value (hash value of the previous block and nonce) for the current block, that is less than or equal to the difficulty value set by the system, a process known as mining.
This work encounters selfish mining attacks in bitcoin mining pools, launches selfish mining attacks, mitigates the attack and devises the optional stopping time for a miner to quit mining. The classification of the crypto addresses belonging to a mining pool or individual miners, the miners within the pool being clustered based on their hash rate to infer their computational power is done as the preliminary analysis.
There are two primary phases one is classification of crypto addresses with notable accuracy of 99.47% accuracy with 1,53,011 observations which surpasses Kaggle’s 98.93% accuracy with 22,000 observations. Clustering of the crypto addresses to group miners with similar computational power within mining pools is the second phase.
This clustering yielded a silhouette coefficient value of 0.5 for all four clusters. The launching of the selfish mining attack, predicts relative gain, from the NIST dataset with the root mean square for the deep learning model as 0.0565. In addition, the prediction of miners' block rewards, yielding a root mean square error of 1.336 and an R-squared value of 0.0059 is devised.
The upper confidence bound algorithm was tailored to mitigate the selfish mining attack with generation and plot of upper confidence bound values, regret graph, and revenue graph plotted and compared with previous results from the literature. The lemmas concerning the potential ruin of honest miners are then investigated and the concept of optional stopping time, to cease mining to prevent complete ruin, is thoroughly examined and elucidated.
Recommended Citation
M J, Jeyasheela Rakkini Ms, "Encountering and Mitigating Selfish Mining in Bitcoin Mining Pools" (2025). Theses and Dissertations. 7.
https://knowledgeconnect.sastra.edu/theses/7