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In order to improve the revenues of attack mining pools and miners under block withholding attack, we propose the mining revenue optimization algorithm (MROA) of miners in PoW-based blockchain network. MROA establishes the revenue optimization model of each attack mining pool and revenue optimization model of entire mining attack pools under block withholding attack with the mathematical formulas such as attack mining pool selection, effective computing power, mining cost and revenue. Then MROA solves the model by using the modified artificial bee colony algorithm based on Pareto. Namely, employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto domination calculation, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto domination calculation, and can find the optimal food source in multidimensional space with smaller distribution density. Scout bee operations delete the local optimal food source which cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attack mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attack mining pool, which improves minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state-of-arts such as ABC, NSGA2 and MOPSO.
Keywords
Block withholding attack, Blockchain, Pow, Mining cost
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CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
Version 1
Posted 13 Nov, 2020
No community comments so far
Editorial decision: Major revision
On 31 Mar, 2021
Review #3 received
Received 29 Mar, 2021
Reviewer #4 agreed
On 09 Feb, 2021
Review #2 received
Received 11 Jan, 2021
Reviewer #3 agreed
On 10 Jan, 2021
Review #1 received
Received 24 Dec, 2020
Reviewer #2 agreed
On 16 Dec, 2020
Reviewers invited
Invitations sent on 25 Nov, 2020
Reviewer #1 agreed
On 25 Nov, 2020
Editor assigned
On 10 Nov, 2020
Editor invited
On 10 Nov, 2020
Submission checks complete
On 10 Nov, 2020
First submitted
On 06 Nov, 2020
Metrics
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Subject Areas
Systems and Networking
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A new preprint design is coming!
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See the published version of this preprint at EURASIP Journal on Wireless Communications and Networking.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
Version 1
Posted 13 Nov, 2020
No community comments so far
Editorial decision: Major revision
On 31 Mar, 2021
Review #3 received
Received 29 Mar, 2021
Reviewer #4 agreed
On 09 Feb, 2021
Review #2 received
Received 11 Jan, 2021
Reviewer #3 agreed
On 10 Jan, 2021
Review #1 received
Received 24 Dec, 2020
Reviewer #2 agreed
On 16 Dec, 2020
Reviewers invited
Invitations sent on 25 Nov, 2020
Reviewer #1 agreed
On 25 Nov, 2020
Editor assigned
On 10 Nov, 2020
Editor invited
On 10 Nov, 2020
Submission checks complete
On 10 Nov, 2020
First submitted
On 06 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems and Networking
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at EURASIP Journal on Wireless Communications and Networking.
In order to improve the revenues of attack mining pools and miners under block withholding attack, we propose the mining revenue optimization algorithm (MROA) of miners in PoW-based blockchain network. MROA establishes the revenue optimization model of each attack mining pool and revenue optimization model of entire mining attack pools under block withholding attack with the mathematical formulas such as attack mining pool selection, effective computing power, mining cost and revenue. Then MROA solves the model by using the modified artificial bee colony algorithm based on Pareto. Namely, employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto domination calculation, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto domination calculation, and can find the optimal food source in multidimensional space with smaller distribution density. Scout bee operations delete the local optimal food source which cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attack mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attack mining pool, which improves minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state-of-arts such as ABC, NSGA2 and MOPSO.
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The full text of this article is available to read as a PDF.
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