Delay Impact on Stubborn Mining Attack Severity in Imperfect Bitcoin Network
Bibliographic record
Abstract
Bitcoin is the largest Proof-of-Work (PoW) public blockchain but is vulnerable to various attacks like stubborn mining attack, which greatly downgrades both system throughput and benefits malicious miners (attackers). The existing works assume miners receive new blocks immediately after block generation, which is away from reality. This article aims to quantify the stubborn mining attack severity in an imperfect Bitcoin network in which there exists block receiving delay. In this article, we first develop an analytic model to capture blockchain dynamics, and then derive formulas of both relative revenue and system throughput, which are applied to study attack severity. Experiment results validate our quantitative analysis method and show that imperfect networks favor attackers. Moreover, the results recommend a blockchain system to be composed of small mining pools to get fair revenue distribution, and minimize its network delay and fork probability to get high TPS.
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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 | 0.005 |
| 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".