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

Hossein Abbasi, Naser Ezzati-Jivan, Martine Bellaïche, Chamseddine Talhi and Michel R. Dagenais

Article (2019)

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Cite this document: Abbasi, H., Ezzati-Jivan, N., Bellaïche, M., Talhi, C. & Dagenais, M. R. (2019). Machine learning-based EDoS attack detection technique using execution trace analysis. Journal of Hardware and Systems Security, 3(2), p. 164-176. doi:10.1007/s41635-018-0061-2
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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.

Uncontrolled Keywords

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

Open Access document in PolyPublie
Subjects: 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
Department: Département de génie informatique et génie logiciel
Research Center: Non applicable
Grant number: CRDPJ507883-16
Date Deposited: 02 Mar 2020 13:17
Last Modified: 03 Mar 2020 01:20
PolyPublie URL: https://publications.polymtl.ca/4211/
Document issued by the official publisher
Journal Title: Journal of Hardware and Systems Security (vol. 3, no. 2)
Publisher: Springer
Official URL: https://doi.org/10.1007/s41635-018-0061-2


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