A Strong Adaptive, Strategic Double-Spending Attack on Blockchains
Bibliographic record
Abstract
In this paper, we first propose an adaptive strategy for double-spending attack on blockchains. The attacker in our strategy observes the length of the honest branch when a submitted transaction becomes available in the blockchain, and then updates the attack strategy accordingly. This provides a stronger strategy than conventional double-spending attack. We then derive closed-form expressions for the probability of a successful attack and the expected reward of attacker miners. Our analysis shows that the probability of a successful attack by convincing the network nodes to follow the counterfeit branch under the proposed attack strategy is 60% higher than what is expected from the conventional attack strategy when the attackers acquire 40% of the total network processing power. To counter this increase in the probability of attack, the network nodes are required to use a bigger number of confirmation blocks for validating any transaction in the blockchain. We computed the. expected reward of an attacker for mining a counterfeit branch on a blockchain and observed that the expected reward drops to zero after a few number of block confirmations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".