Is Stubborn Mining Severe in Imperfect GHOST Bitcoin-Like Blockchains? Quantitative Analysis
Why this work is in the frame
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Bibliographic record
Abstract
GHOST, like the longest-chain protocol, is a chain selection protocol and its capability in resisting selfish mining attack has been validated in imperfect (delay-existing-) blockchains of Bitcoin and its variants (Bitcoin-like). This paper explores an analytical-model-based approach to investigate the impact of stubborn mining attack in imperfect GHOST Bitcoin-like blockchains. We first quantify chain dynamics based on Markov chain process and then derive the formulas of miner revenue and system throughput. We also propose a new metric, “Hazard Index”, which can be used to evaluate attack threat severity and also assist the adversary in determining whether it is profitable to conduct an attack. The experiment results show that 1) An adversary with more than 30% computing power can get huge profit and extremely downgrade system throughput by launching stubborn mining attack. 2) An adversary should not launch stubborn mining attack if it has less than 25% computing power. 3) Stubborn mining attack causes more damage than selfish mining attack under GHOST. Our work provides insight into stubborn mining attack and is helpful in designing countermeasures.
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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 | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it