Please use this identifier to cite or link to this item: http//localhost:8080/jspui/handle/123456789/1866
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGuenez, Yamina-
dc.date.accessioned2022-03-08T10:49:56Z-
dc.date.available2022-03-08T10:49:56Z-
dc.date.issued2020-
dc.identifier.urihttp//localhost:8080/jspui/handle/123456789/1866-
dc.description.abstractThe security of our data and systems was and will always be the main subject that we‘re trying to tackle, especially with the fast growth of technology in different fields such as mobile cloud computing. This fast growth is always accompanied by serious security issues that threaten our data privacy and integrity. That’s why we need an effective approach of detection in order to prevent those cyber threats and protect our data in an efficient way. In this work, we used a promising Deep Learning approach based on CNN 1D (Convolutional Neural Networks) with different architectures to tackle these kinds of security issuesen_US
dc.description.sponsorshipAmroune Mohameden_US
dc.language.isoenen_US
dc.publisherLarbi Tbessi University – Tebessaen_US
dc.subjectCybersecurity; cyberattack; mobile cloud computing; deep learning; CNN 1Den_US
dc.subjectcyber-sécurité; cyber-attaque; cloud computing mobile; l'apprentissage profond; CNN 1D.en_US
dc.subjectاألمن السيبراني ؛ الهجمات األلكترونية ؛ الحوسبة السحابية المتنقلة ؛ التعلم العميق؛ 1D CNNen_US
dc.titleCyberattack detection in mobile cloud computing: a deep learning approachen_US
dc.typeThesisen_US
Appears in Collections:3- إعلام آلي



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Admin Tools