Evaluating Checkpointing Capability against Eclipse-based Stake-Bleeding Attack in PoS Blockchain
Bibliographic record
Abstract
Eclipse-based Stake-Bleeding (ESB) attack is a kind of long-range attacks in a Proof-of-Stake (PoS) blockchain system, which can shorten the attack completion time. Researchers have quantitatively analyzed ESB attack but ignoring checkpointing defense technique, which solidifies the history of blockchain and then prevents an adversary to change the blockchain. This paper aims to investigate the capability of the checkpoint-based defense scheme in resisting ESB attack. We develop a Monte Carlo simulator to capture the dynamics of a PoS blockchain system, which is subject to ESB attack and deploys checkpoint-base defense scheme. We also develop the methods for calculating the probability of successful attack and attack profit. This simulator can be applied to evaluate the influence of ESB attack and the checkpointing capability from the perspective of honest verifiers, adversaries and victims. Our experiment results can help enhance PoS blockchain security.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".