A Theoretical Analysis of Cloud Firewall Under Bursty and Correlated Data Traffic

PROJECT TITLE :

Cloud Firewall Under Bursty and Correlated Data Traffic A Theoretical Analysis

ABSTRACT:

Cloud firewalls are an important component of the overall cloud security framework, serving to protect the Virtual Private Infrastructure from threats such as distributed denial of service attacks (DDoS). It is absolutely necessary to develop performance models for the cloud firewall if one wishes to completely characterize the operation of the cloud firewall and obtain actionable insights regarding the architecture of cloud security. We propose a multi-dimensional Continuous-Time Markov Chain model for the cloud firewall in this article. This model takes into account the burstiness and correlation characteristics of both legitimate and malicious data traffic. The model is intended to protect the cloud from threats. By using the Markov-Modified Poisson process (MMPP) and the Interrupted Poisson Process (IPP), we were able to determine the workload conditions that could lead to a loss of availability for the cloud firewall. In addition, we numerically verify that the cloud firewall is inherently vulnerable to a burstiness-aware attack by contrasting the IPP attack with the Poisson attack. This vulnerability could seriously compromise the cloud firewall's ability to do its job. In addition, we have characterized the potentially detrimental effects of burstiness and correlation acting together on the cloud firewall, which may result in a decrease in performance. In the end, we design an elastic cloud firewall by proposing an MMPP-driven load balancing procedure that can provision virtual firewalls in a dynamic manner while still meeting a Service Level Agreement (SLA) latency specification. This allows us to fulfill a SLA.

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