Authors: Audecious Mugwagwa, Colin Chibaya, Ernest Bhero
Affiliation: University of KwaZulu Natal, Sol Plaatje University


Keywords: cybersecurity, cyberthreats, self-organization, swarm intelligence, algorithms
ABSTRACT. Because most attacks target computers, intrusion detection has emerged as a key component of network security. This is a result of the widespread expansion of internet connectivity and information system accessibility on a global scale. The Wolf Sheep Predation Algorithm (WSPA), evolved from the Wolf Pack Algorithm. It models how wolves hunt in packs. This paper focused on the Lotka-Volterra predator-prey model. Due to its global convergence and computational strength, it has mostly been applied in a variety of engineering optimization issues. The method, however, has numerous flaws, including slow convergence and a tendency to quickly reach the local optimum. To address the above-mentioned flaws, this research developed the Modified Wolf Sheep Predation Algorithm (MWSPA) to reduce network threats. the algorithm models the wolves and sheep, where the wolves in this study represent the network security agent while the sheep represent network threats. The model suggests a better strategy to address the problem of slow convergence and quickly reach the local optimum by making sure that there is a balanced ecosystem at any point in time. This is achieved by ensuring that the network security agents(wolves) are not outnumbered by threats(sheep) and they do not become extinct when there is no food source. So in the absence of food, the MWSPA ensures the wolves can survive on grass and maintain their strength to hunt their next prey. This idea prevents the algorithm from crashing if the wolves die while the prey grows to infinity and consumes all the available grass. This therefore solves the problem of rapidly failing into a local optimum. This study aimed to identify the most pertinent features employed by wolves (network security agents) while hunting the sheep (network threats). We therefore established that sense of hearing and smell, splitting prey, encircling prey, assisting the hunter with the best chance of success, and looking for alternative prey as the most outstanding attributes used by wolves while hunting. The study further evaluated the MWSPA, and the outcomes demonstrate that the suggested algorithm outperforms its predecessor approach in a variety of search environments. Therefore, this shows that the MWSPA may possess the necessary qualities for creating a solution that will completely eradicate network threats and might provide leads in solving growing cybersecurity concerns globally.


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