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MoneroResearch.info |
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Purkovic, S., Mekic, E., Kuk, K., & Gostimirovic, L. (2021). Empirical Analysis of Silent Mining Operation in the Monero System, Studies in Informatics and Control, 30(4), 99–108. Added by: Jack (20/01/2023, 03:08) Last edited by: Jack (20/01/2023, 18:21) |
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| Resource type: Journal Article BibTeX citation key: Purkovic2021 View all bibliographic details  |
Categories: Monero-focused Creators: Gostimirovic, Kuk, Mekic, Purkovic Collection: Studies in Informatics and Control |
Views: 98/2187
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Attachments
Empirical analysis of silent mining ops.pdf  [17/766]
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URLs https://sic.ici.ro ... the-monero-system/ |
| Abstract |
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This paper analyses three important issues regarding Blockchain systems. The first one is related to the existence, success and mitigation of silent mining activity achieved through the development of Application-specific Integrated Circuits (ASICs). The second one lies in the mathematical modelling of Blockchain systems affected by ASIC mining machines and in the mathematical modelling of Blockchain with suppressed ASICs. Finally, this paper presents the economic parameters related to the rate of Return on Investment (ROI) and the possibility of calculating them based on the obtained results. Three different Blockchain systems were analysed, two of which allow the usage of ASIC machines, while one of them by definition does not support this type of mining activity. The analysis showed that the systems which involve ASIC machines can be described by means of linear regression models, while suppressing ASICs mining would provide a different statistical model. Successful mitigation activities can provide reliable data for the calculation of the economic parameters related to the rate of Return on Investment (ROI) based on silent mining
Added by: Jack Last edited by: Jack |
| Notes |
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https://sic.ici.ro/wp-content/uploads/2021/12/Art.-9-Issue-4-2021.pdf
This paper concluded that there were "secret" ASICs mining CryptoNight PoW hash functions and modeled the system using linear regression. The paper predates the Random-X hashing algorithm Added by: Jack Last edited by: Jack |