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Machine Learning-Based EDoS Attack Detection Technique Using Execution Trace Analysis

  • Hossein AbbasiEmail author
  • Naser Ezzati-Jivan
  • Martine Bellaiche
  • Chamseddine Talhi
  • Michel R. Dagenais
Article
  • 15 Downloads

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.

Keywords

DDoS attacks EDoS attacks Cloud computing Machine learning Detection 

Notes

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ecole Polytechnique MontrealMontrealCanada
  2. 2.École de Technologie SupérieureMontrealCanada

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