Cyberattack detection in mobile cloud computing: a deep learning approach

dc.contributor.author	Guenez, Yamina
dc.date.accessioned	2022-03-08T10:49:56Z
dc.date.available	2022-03-08T10:49:56Z
dc.date.issued	2020
dc.identifier.uri	http//localhost:8080/jspui/handle/123456789/1866
dc.description.abstract	The 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 issues	en_US
dc.description.sponsorship	Amroune Mohamed	en_US
dc.language.iso	en	en_US
dc.publisher	Larbi Tbessi University – Tebessa	en_US
dc.subject	Cybersecurity; cyberattack; mobile cloud computing; deep learning; CNN 1D	en_US
dc.subject	cyber-sécurité; cyber-attaque; cloud computing mobile; l'apprentissage profond; CNN 1D.	en_US
dc.subject	األمن السيبراني ؛ الهجمات األلكترونية ؛ الحوسبة السحابية المتنقلة ؛ التعلم العميق؛ 1D CNN	en_US
dc.title	Cyberattack detection in mobile cloud computing: a deep learning approach	en_US
dc.type	Thesis	en_US