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Cyberattack detection in mobile cloud computing: a deep learning approach

dc.contributor.authorGuenez, Yamina
dc.date.accessioned2022-03-08T10:49:56Z
dc.date.available2022-03-08T10:49:56Z
dc.date.issued2020
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.provenanceSubmitted by snv snv (dspace.snv@univ-tebessa.dz) on 2022-03-08T10:49:56Z No. of bitstreams: 1 pfe_gunez_yamina_master2info_Cyberattack detection in mobile cloud computing a deep learning approach.pdf: 2882761 bytes, checksum: bae6fb1c091210295b775668511d7a5c (MD5)en
dc.description.provenanceMade available in DSpace on 2022-03-08T10:49:56Z (GMT). No. of bitstreams: 1 pfe_gunez_yamina_master2info_Cyberattack detection in mobile cloud computing a deep learning approach.pdf: 2882761 bytes, checksum: bae6fb1c091210295b775668511d7a5c (MD5) Previous issue date: 2020en
dc.description.sponsorshipAmroune Mohameden_US
dc.identifier.urihttp//localhost:8080/jspui/handle/123456789/1866
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

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