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Machine learning-based EDoS attack detection technique using execution trace analysis

Hossein Abbasi, Naser Ezzati-Jivan, Martine Bellaïche, Chamseddine Talhi et Michel Dagenais

Article de revue (2019)

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Abstract

One of the most important benefits of using cloud computing is the benefit of on-demand services. Accordingly, the method of payment in the cloud environment is pay per use. This feature results in a new kind of DDOS attack called Economic Denial of Sustainability (EDoS), in which the customer pays extra to the cloud provider as a result of the attack. Similar to other DDoS attacks, EDoS attacks are divided into different types, such as (1) bandwidth-consuming attacks, (2) attacks that target specific applications, and 3) connection-layer exhaustion attacks. In this work, we propose a novel framework to detect different types of EDoS attacks by designing a profile that learns from and classifies the normal and abnormal behaviors. In this framework, the extra demanding resources are only allocated to VMs that are detected to be in a normal situation and therefore prevent the cloud environment from attack and resource misuse propagation.

Mots clés

DDoS attacks; EDoS attacks; cloud computing; machine-learning; detection

Sujet(s): 2700 Technologie de l'information > 2706 Génie logiciel
2700 Technologie de l'information > 2715 Optimisation
2800 Intelligence artificielle > 2805 Théories de l'apprentissage et de l'inférence
Département: Département de génie informatique et génie logiciel
Organismes subventionnaires: CRSNG/NSERC
Numéro de subvention: CRDPJ507883-16
URL de PolyPublie: https://publications.polymtl.ca/4211/
Titre de la revue: Journal of Hardware and Systems Security (vol. 3, no 2)
Maison d'édition: Springer
DOI: 10.1007/s41635-018-0061-2
URL officielle: https://doi.org/10.1007/s41635-018-0061-2
Date du dépôt: 02 mars 2020 13:17
Dernière modification: 14 mai 2023 20:03
Citer en APA 7: Abbasi, H., Ezzati-Jivan, N., Bellaïche, M., Talhi, C., & Dagenais, M. (2019). Machine learning-based EDoS attack detection technique using execution trace analysis. Journal of Hardware and Systems Security, 3(2), 164-176. https://doi.org/10.1007/s41635-018-0061-2

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