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Conventional intrusion detection systems encounter difficulties in addressing advanced cyber threats and handling the increasing volume of network data.  This research presents a modernisation strategy by integrating swarm intelligence algorithms to enhance the efficiency and efficacy of intrusion detection.  This research employs qualitative observational and content analysis methodologies to investigate the utilisation of swarm intelligence in improving intrusion detection systems.  Findings demonstrate substantial enhancements in detection rates and system efficacy, with swarm intelligence algorithms attaining a true positive detection rate of over 99% and minimising false positives to as low as 2%.  These findings highlight the impending substitution of conventional intrusion detection systems with swarm intelligence-based alternatives, offering significant enhancement in cybersecurity capabilities.

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.

Conventional intrusion detection systems encounter difficulties in addressing advanced cyber threats and handling the increasing volume of network data.  This research presents a modernisation strategy by integrating swarm intelligence algorithms to enhance the efficiency and efficacy of intrusion detection.  This research employs qualitative observational and content analysis methodologies to investigate the utilisation of swarm intelligence in improving intrusion detection systems.  Findings demonstrate substantial enhancements in detection rates and system efficacy, with swarm intelligence algorithms attaining a true positive detection rate of over 99% and minimising false positives to as low as 2%.  These findings highlight the impending substitution of conventional intrusion detection systems with swarm intelligence-based alternatives, offering significant enhancement in cybersecurity capabilities.

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.