Comparative Analysis of State-of-the-Art EDoS Mitigation Techniques in Cloud Computing Environment
This addresses a major security implication for cloud service providers facing EDoS attacks, but it appears incremental as it builds on existing mitigation methods with enhancements.
The paper tackles the problem of Economic Denial of Sustainability (EDoS) attacks in cloud computing, which exploit pay-per-use models to cause financial unsustainability, and proposes an Enhanced Mitigation Mechanism (EMM) that achieves high accuracy in detection and mitigation while being effective in resource utilization compared to existing techniques.
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.