Prevention and Detection Mechanisms for Re-Entrancy Attack and King of Ether Throne Attack for Ethereum Smart Contracts
Bibliographic record
Abstract
The second revolution in blockchain technology is smart contracts. Smart contracts are used in most of the blockchain applications like cryptocurrency, Health care, banking sectors, supply chain and IOT with different platforms like Fabric, Ethereum, Corda etc. In Ethereum blockchain, due to lack of inefficiency of the knowledge of technical developers and insecure programming languages for smart contracts, the attackers have exploited the smart contracts and the end users have lost millions of dollars like re-entrancy, king of ether throne attack, DoS, forcefully send ethers, multisig wallet, unexpected ether and poly network attack etc. In the year 2016, the attackers have exploited approximately $289 million US dollars with the help of re-entrancy vulnerability. The attackers have also attacked the smart contracts and broke the execution of that particular contracts through king of ether throne attack. In this paper, we propose a novel prevention and detection mechanisms for re-entrancy and king of ether throne attacks using time mechanisms and also implementing the same with proof of concepts for these vulnerabilities.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".