How Does FAW Attack Impact an Imperfect PoW Blockchain: A Simulation-based Approach
Bibliographic record
Abstract
Malignant miners with small computing power can achieve unfair revenue and degrade system throughput through launching Fork after withholding (FAW) attack in a Proof-of-Work (PoW) blockchain system. The existing works about FAW attack have some of the following issues: (i) only studying Bitcoin blockchain, (ii) assuming that the blockchain network is perfect and then ignoring forks due to block propagation delay, and (iii) assuming that there is only one pool under attack. This paper attempts to investigate FAW attack in imperfect Bitcoin and Ethereum networks where malicious miners attack multiple victim pools. We develop a simulator to capture the chain dynamics under FAW attack in a PoW system where the longest-chain protocol is used. Two different computing power allocation strategies for malicious miners, PAS and EAS, are investigated in terms of the profitability of FAW adversaries, the loss of victims, and the blockchain throughput. The results reveal that FAW adversaries can get more revenue under PAS when more victim pools are subjected to attack in both Bitcoin and Ethereum. If FAW adversaries adopt EAS and the number of victims vary from 1 to 12, they can get maximal revenue when attack 7 victims in Bitcoin. The blockchain throughput decreases significantly under PAS while it is almost unchanged under EAS with the increasing number of victims in both Bitcoin and Ethereum. Our work helps the design of countermeasures against FAW attack.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".