Automated Modified Grey Wolf Optimizer for Identification of Unauthorized Requests in Software-defined Networks

Abstract

Software Defined Networking (SDN) is utilized to centralize network control within a controller, but its reliance on a single control plane can make it vulnerable to attacks such as DDoS. This highlights the importance of developing effective security mechanisms and using proactive measures such as detection and prevention strategies to mitigate the risk of attacks. Many DDoS attack detection technologies within SDN focus on detecting and mitigating the attack once it has occurred in the controller, which leads to more seconds of exposure, diminished precision, and high overhead. In this work, we have developed an Automated Modified Grey Wolf Optimizer Algorithm (AMGWOA) to design the detection of this malicious activity in an SDN environment to prevent the attack in the controller. Our methodology involves the development of the AMGWOA, which incorporates a mechanism to facilitate the blocking of malicious requests while reducing detection time and minimizing the use of storage and data resources for detection purposes. The results obtained show that our model performs well, with an ability to minimize a very large number of malicious requests in a minimum of time of less than 1 second compared to Grey Wolf Optimizer and particle swarm optimization algorithms evaluated using the same datasets.