Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/519572
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | Network optimization and security enhancement for cloud based e learning platforms | |
dc.date.accessioned | 2023-10-22T05:19:18Z | - |
dc.date.available | 2023-10-22T05:19:18Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/519572 | - |
dc.description.abstract | newline This study compared the performance of various cryptographic algorithms, including Elliptic Curve Cryptography (ECC), Advanced Encryption Standard (AES), Two Fish, Blowfish, Data Encryption Standard (DES), Triple Data Encryption Standard (TDES), and role-based access control, to ensure the security of cloud data storage used for educational purposes in the NPTEL database (RBAC). The cloud-based network optimization data transmission is explained by network optimization ROA. The performance of this technique has been evaluated using a variety of requests and metrics. Compared to PSO, GA, and Firefly, we consumed less RAM. This ROA manages incremental server load sharing to keep servers from becoming overwhelmed or idle. Based on availability and comparison logic, it ensures that new users are assigned to the most powerful server. | |
dc.format.extent | xviii, 142 p. | |
dc.language | English | |
dc.relation | p. 124-141 | |
dc.rights | university | |
dc.title | Network optimization and security enhancement for cloud based e learning platforms | |
dc.title.alternative | ||
dc.creator.researcher | Soundhara Raja Pandian R | |
dc.subject.keyword | E-Learning | |
dc.subject.keyword | Engineering | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Engineering Electrical and Electronic | |
dc.subject.keyword | NPTEL | |
dc.subject.keyword | Rider Optimization Algorithm | |
dc.description.note | ||
dc.contributor.guide | Christopher Columbus C and Sree Rathna Lakshmi N V S | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Electrical Engineering | |
dc.date.registered | ||
dc.date.completed | 2023 | |
dc.date.awarded | 2023 | |
dc.format.dimensions | 21 cm. | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Electrical Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 29.6 kB | Adobe PDF | View/Open |
02_prelim_pages.pdf | 387.06 kB | Adobe PDF | View/Open | |
03_content.pdf | 26.79 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 20.37 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 337.73 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 91.79 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 374.71 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 800.34 kB | Adobe PDF | View/Open | |
09_annexures.pdf | 149.05 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 91.06 kB | Adobe PDF | View/Open |
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