A survey of inspiring swarm intelligence models for the design of a swarm-based ontology for addressing the cyber security problem

Audecious Mugwagwa, Colin Chibaya and Ernest Bhero
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Audecious Mugwagwa: University of KwaZulu-Natal, Durban, South Africa
Colin Chibaya: Sol Plaatje University, Kimberley, South Africa
Ernest Bhero: University of KwaZulu-Natal, Durban, South Africa

International Journal of Research in Business and Social Science (2147-4478), 2023, vol. 12, issue 4, 483-494

Abstract: The increased use of the internet raises concerns about the security of data and other resources shared in cyberspace. Although efforts to improve data security are visible, the need to continuously explore other avenues for preventing and mitigating cyberattacks is apparent. Swarm intelligence models have, in the past, been considered in cybersecurity though there was no formal representation of the swarm intelligence knowledge domain that defines how these models fit into the cybersecurity body of knowledge. This article reviews the aspects of three swarm intelligence models that may inspire the design of the desired swarm intelligence ontology. The algorithms are particle swarm optimization, ant colony optimization, and the artificial bee colony model. In each case, we investigate the main driving features of the model, the causal aspects, and the effects of those causal aspects on the resolution of the cybersecurity problem. We also investigate how these features can be recommended as the building blocks of the desired swarm intelligence ontology. Investigations indicate that the artificial bee colony model has three outstanding aspects considered for the design of the swarm intelligence ontology and that is the quality, popularity, and communication. Foraging through pheromone deposits is an outstanding component of ant colony optimization that aids in locating threats sources more quickly by using the shortest route or tracks with high pheromone deposits. The particle swarm optimization model, on the other hand, adds alignment, cohesion, and collision avoidance aspects to the ontology to augment the ant colony and artificial bee colony algorithms. In our view, although intrusion detection is a complex problem in cybersecurity, the power of integrated swarm intelligence models is more than the sum of the individual capabilities of each swarm intelligence model individually. The article, therefore, proposes a swarm intelligence ontology that will potentially bring us closer to resolving the general cybersecurity problem. Key Words:Swarm Intelligence, Cybersecurity, Particle Swarm Optimisation, Ant Colony Model, Artificial Bee Colony Model, Swarm Intelligence Ontology

Date: 2023
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