Mitigating Economic Denial of Sustainability Attacks to Secure Cloud Computing Environments

  • Marwane Zekri IPOSI laboratory National School of Applied Sciences, Hassan 1st Univ, Settat, Morocco
  • Said El Kafhali Computer, Networks, Mobility and Modeling laboratory National School of Applied Sciences, Hassan 1st Univ, Settat, Morocco
  • Mohamed Hanini Computer, Networks, Mobility and Modeling laboratory Faculty of Sciences and Technology, Hassan 1st Univ, Settat, Morocco
  • Noureddine Aboutabit IPOSI laboratory National School of Applied Sciences, Hassan 1st Univ, Settat, Morocco
Keywords: component, Security, Cloud Computing, DDoS attack, EDoS attack, Mitigation

Abstract

In cloud computing environment where the infrastructure is shared by millions of users, attackers have the opportunity to ensure more damage to the compromised resources. The main aim of such attacks is to saturate and overload the system network through a massive data packets size flooding toward a victim server and to block the service to customers. The Distributed Denial-of-service (DDoS) attack is considered one of the largest threats to the Quality of Service (QoS) of cloud services which is used to deny access for legitimate users of an online service. However, Economic Denial of Sustainability (EDoS) attack is a special breed of DDoS attack that attack exploits auto scaling feature of cloud. The Cloud Service Provider (CSP) scales the architecture automatically to serve those requests for which cloud consumer is charged. A consumer expects a sustainable profit after hosting his application on cloud. The attacker purpose is to guarantee the service unavailability and maximize the financial loss costs by increasing the cost and decreasing the profit. Hence, in this paper we propose a novel mitigation system against the EDoS attacks. Our system consists of source Checking, Counting, and Turing Test. The obtained simulation results show that our system works efficiently to mitigate the EDoS attack in cloud environment.

 

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Published
2017-09-01
Section
Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems