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International Conference on Machine Learning for Networking

MLN 2018: Machine Learning for Networking pp 79–97Cite as

Advanced Hybrid Technique in Detecting Cloud Web Application’s Attacks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11407))

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.

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Author information

Authors and Affiliations

  1. Mohammed-V University, I.P.S.S, Rabat, Morocco

    Meryem Amar, Mouad Lemoudden & Bouabid El Ouahidi

Authors
  1. Meryem Amar
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  2. Mouad Lemoudden
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  3. Bouabid El Ouahidi
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Corresponding author

Correspondence to Meryem Amar .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Évry, France

    Éric Renault

  2. Inria, Paris, France

    Paul Mühlethaler

  3. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

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Amar, M., Lemoudden, M., El Ouahidi, B. (2019). Advanced Hybrid Technique in Detecting Cloud Web Application’s Attacks. In: Renault, É., Mühlethaler, P., Boumerdassi, S. (eds) Machine Learning for Networking. MLN 2018. Lecture Notes in Computer Science(), vol 11407. Springer, Cham. https://doi.org/10.1007/978-3-030-19945-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-19945-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19944-9

  • Online ISBN: 978-3-030-19945-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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