Comparative Analysis of State-of-the-Art EDoS Mitigation Techniques in Cloud Computing Environment

TL;DR

This paper presents a new mitigation mechanism for Economic Denial of Sustainability (EDoS) attacks in cloud computing, using OpenFlow and statistical techniques to improve detection accuracy and resource efficiency.

Contribution

It introduces an Enhanced Mitigation Mechanism (EMM) that effectively detects and mitigates EDoS attacks with high accuracy and low resource consumption, outperforming existing methods.

Findings

EMM achieves high detection accuracy for EDoS attacks.

EMM reduces resource utilization compared to existing techniques.

The proposed method can be deployed without additional resource requirements.

Abstract

A new variant of the DDoS attack, called Economic Denial of Sustainability 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 Mitigation Mechanism 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, can be deployed in the cloud environment without the need for additional resource requirements.

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