Performance Analysis of EMM an EDoS Mitigation Technique in Cloud Computing Environment

As many organizations are adopting cloud computing as this allows them to be more agile, flexible and efficient. Nevertheless, as with any new technologies, cloud computing also suffers from various issues especially in security and privacy. Distributed Denial of Service (DDoS) attack saturates server resources, e.g. web server, by flooding it with fake requests. This renders the server inaccessible to legitimate users. Nevertheless, if the server is hosted as a cloud service, DDoS attack will not be effective due to the elasticity nature of the cloud server. Thus, a new variant of the DDoS attack, called Economic Denial of Sustainability (EDoS) attack, has emerged. Since the cloud service is based on “pay-per-use” model, EDoS attack endeavors to scale up the resource usage over time to the point the purveyor of the server is financially incapable of sustaining the service due to the incurred unaffordable usage charges. The implication of EDoS attack is a major security implication as more elastic cloud services are being deployed. A new mechanism, EDoS Mitigation Mechanism (EMM), is proposed to address these shortcomings using OpenFlow and statistical techniques, i.e. Hellinger Distance and Entropy. The experiments clearly showed that EMM is able to detect and mitigate EDoS attacks effectively without the need for additional resource requirements.