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Advanced Hybrid Technique in Detecting Cloud Web Application’s Attacks

Amar, Meryem; Lemoudden, Mouad; El Ouahidi, Bouabid

Authors

Meryem Amar

Bouabid El Ouahidi



Abstract

Recently cloud computing has emerged the IT world. It eventually promoted the acquisition of resources and services as needed, but it has also instilled fear and user’s renunciations. However, Machine learning processing has proven high robustness in solving security flaws and reducing false alarm rates in detecting attacks. This paper, proposes a hybrid system that does not only labels behaviors based on machine learning algorithms using both misuse and anomaly-detection, but also highlights correlations between network relevant features, speeds up the updating of signatures dictionary and upgrades the analysis of user behavior.

Presentation Conference Type Conference Paper (Published)
Conference Name International Conference on Machine Learning for Networking
Start Date Nov 27, 2018
End Date Nov 29, 2018
Online Publication Date May 10, 2019
Publication Date 2019
Deposit Date Feb 28, 2023
Publisher Springer
Pages 79-97
Series Title Lecture Notes in Computer Science
Series Number 11407
Series ISSN 1611-3349
Book Title Machine Learning for Networking: First International Conference, MLN 2018, Paris, France, November 27–29, 2018, Revised Selected Papers
ISBN 978-3-030-19944-9
DOI https://doi.org/10.1007/978-3-030-19945-6_6
Keywords Attack-detection, Cloud, IDS, Machine learning, Security, Similarities