Model for Determining the Cloud Computing Services Vulnerability Level; Lecture Notes in Electrical Engineering (LNEE); Vol. 986 : Advances in Automation IV
| Parent link: | Lecture Notes in Electrical Engineering (LNEE) Vol. 986 : Advances in Automation IV.— 2023.— [P. 119-126] |
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| מחבר ראשי: | |
| מחבר תאגידי: | |
| סיכום: | Title screen In decision making when comparing alternatives, it is often necessary to work with different values and scores. Binary relations and graph theory proved to be efficient when conducting this type of analysis. Cloud computing services, like any other technical means, are exposed to vulnerability and prone to breakdowns. This article proposes a model for determining the vulnerability level of cloud computing sections based on the ELECTRE I method. This model allows identifying sections that are most at risk of breakdown or failure, as well as removing dominant alternatives that do not constitute a grave danger to the system. Based on identified preferences, it is possible to decide on discrepancies rectification in the selected sections of the cloud computing services. Режим доступа: по договору с организацией-держателем ресурса |
| שפה: | אנגלית |
| יצא לאור: |
2023
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| נושאים: | |
| גישה מקוונת: | https://doi.org/10.1007/978-3-031-22311-2_12 |
| פורמט: | אלקטרוני Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=669349 |
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| 200 | 1 | |a Model for Determining the Cloud Computing Services Vulnerability Level |f S. V. Razumnikov | |
| 203 | |a Текст |c электронный | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 20 tit.] | ||
| 330 | |a In decision making when comparing alternatives, it is often necessary to work with different values and scores. Binary relations and graph theory proved to be efficient when conducting this type of analysis. Cloud computing services, like any other technical means, are exposed to vulnerability and prone to breakdowns. This article proposes a model for determining the vulnerability level of cloud computing sections based on the ELECTRE I method. This model allows identifying sections that are most at risk of breakdown or failure, as well as removing dominant alternatives that do not constitute a grave danger to the system. Based on identified preferences, it is possible to decide on discrepancies rectification in the selected sections of the cloud computing services. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Lecture Notes in Electrical Engineering (LNEE) | ||
| 463 | |t Vol. 986 : Advances in Automation IV |o proceedings of the International Russian Automation Conference, RusAutoCon2022, September 4-10, 2022, Sochi, Russia |f eds. A. A. Radionov ; V. R. Gasiyarov |v [P. 119-126] |d 2023 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a computing services | |
| 610 | 1 | |a vulnerability | |
| 610 | 1 | |a ELECTRE I method | |
| 610 | 1 | |a multi-criteria optimization | |
| 610 | 1 | |a non-dominated alternatives | |
| 610 | 1 | |a вычислительные программы | |
| 610 | 1 | |a уязвимость | |
| 610 | 1 | |a оптимизация | |
| 700 | 1 | |a Razumnikov |b S. V. |c specialist in the field of informatics and computer engineering |c Associate Professor of Yurga technological Institute of Tomsk Polytechnic University, Candidate of technical sciences |f 1988- |g Sergey Viktorovich |3 (RuTPU)RU\TPU\pers\34681 |9 18031 | |
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| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-22311-2_12 |z https://doi.org/10.1007/978-3-031-22311-2_12 |
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