The salient features of cloud computing (such as on-demand self-service, resource pooling, broad network access, rapid elasticity, and measured service) are being exploited by attackers to launch the severe Distributed Denial of Service (DDoS) attack. Generally, the DDoS attacks in such an environment have been implemented by flooding a huge volume (high-rate) of malicious traffic to exhaust the victim servers’ resources. Thus, the study of various DDoS attacks and their corresponding defense approaches becomes essential to protect the cloud infrastructure from fatal effects of DDoS attacks. Several prevention, detection, and mitigation approaches against DDoS attacks have been proposed in the literature. However, their performances are not evaluated against the set of common metrics. A set of consistent metrics should be defined to measure the performance of any defense system. This work is a comprehensive comparison of all the possible detection mechanisms of cloud DDoS attacks based on the experimental testbed, adaptivity, True Negative Rate (TNR), True Positive Rate (TPR), and accuracy.
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Agrawal, N., & Tapaswi, S. (2019). Defense mechanisms against DDoS attacks in a cloud computing environment: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 21(4), 3769-3795.