Please use this identifier to cite or link to this item:
http//localhost:8080/jspui/handle/123456789/1866| Title: | Cyberattack detection in mobile cloud computing: a deep learning approach |
| Authors: | Guenez, Yamina |
| Keywords: | Cybersecurity; cyberattack; mobile cloud computing; deep learning; CNN 1D cyber-sécurité; cyber-attaque; cloud computing mobile; l'apprentissage profond; CNN 1D. األمن السيبراني ؛ الهجمات األلكترونية ؛ الحوسبة السحابية المتنقلة ؛ التعلم العميق؛ 1D CNN |
| Issue Date: | 2020 |
| Publisher: | Larbi Tbessi University – Tebessa |
| 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 |
| URI: | http//localhost:8080/jspui/handle/123456789/1866 |
| Appears in Collections: | 3- إعلام آلي |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| pfe_gunez_yamina_master2info_Cyberattack detection in mobile cloud computing a deep learning approach.pdf | 2,82 MB | Adobe PDF | View/Open |
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