Threat Capability of Stubborn Mining in Imperfect GHOST Bitcoin Blockchain
Bibliographic record
Abstract
Bitcoin is the largest PoW blockchain, which currently uses the longest-chain protocol for chain selection and is vulnerable to various attacks like stubborn mining attack. As a variant of selfish mining attack, stubborn mining attack usually has 7 types of strategies, each of which does damage to the blockchain system. GHOST is another chain-selection protocol, which has been demonstrated to make the blockchain system more secure than the longest-chain protocol under selfish mining attack. There were studies on stubborn mining in perfect GHOST blockchains and they only studied two types of stubborn mining strategies. But it is a fact of life that the blockchain is an imperfect network due to ubiquitous network congestion and/or attacks. This paper aims to explore a simulation-based approach to quantitatively evaluate the threat capability of all 7 stubborn mining strategies. We first develop all stubborn strategies in imperfect GHOST blockchains. Then we evaluate miner revenues and system throughput over different network conditions. The results show that the lead-fork-stubborn strategy is the dominant strategy for attackers when they have more than 33% total computing power. The stubborn attackers with less than 20% total computing power lose their revenue whichever stubborn mining strategy is used. The blockchain with high network quality still has the risk of significant throughput downgrade. Our work can help the stubborn mining attack detection and secure blockchain system design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".