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

Abstract

A new variant of the DDoS attack, called Economic Denial of Sustainability (EDoS) attack has emerged. Since the cloud service is based on the pay-per-use model, the 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 the EDoS attack is a major security implication as more elastic cloud services are being deployed. Existing techniques to detect and mitigate such attacks are either have low accuracy or ineffective and, in some cases, aggravate the attack even further. Therefore, an enhanced EDoS Mitigation Mechanism (EMM), is proposed to address these shortcomings using OpenFlow and statistical techniques, i.e. Hellinger Distance and Entropy. The experiments clearly depicted that EMM is able to detect and mitigate EDoS attacks with high accuracy and it is effective in terms of resource utilization compared to existing mitigation techniques. Thus, it can be deployed in the cloud environment without the need for additional resource requirements.

MAN-EDoS: A Multihead Attention Network for the Detection of Economic Denial of Sustainability Attacks

“…Economic denial of sustainability (EDoS) is a kind of denial-of-service (DoS) attack that focuses on the economic aspect [6]. The EDoS attack exploits the cloud computing feature of pay-per-use and causes an unnecessary scale-up of the service, resulting in financial waste for the cloud resource.…”