A Comparative Approach to Mitigate Economic Denial of Sustainability (EDoS) in a Cloud Environment

Abstract

In cloud computing, a new threat is introduced namely Economic Denial of Sustainability (EDoS) which is similar to Distributed Denial of Services (DDoS) attack that targets the vulnerabilities of the cloud server user along with its price model. EDoS attackers continually request bulk resources, such as virtual security devices and databases, virtual machines, virtual networking devices, that targets the auto scaling feature of cloud. As a result, there is a huge increase in consumer bill due to the illegitimate requests which results in bankruptcy. In this paper, various EDoS mitigation techniques are discussed with their different approaches. This paper proposed a new approach that uses Artificial Neural Network along with Genetic Algorithm. The classification of is done using Artificial Neural Network that that classify the cloud server consumer and may lessen the EDoS attacks in the cloud environment whereas Genetic Algorithm (GA) is deployed for optimization of the properties of each server using pertinent fitness function. The proposed technique's comparison with existing technique is taken under the considerations and expectations.