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Modelling intrusion detection systems using swarm intelligence

Author

Listed:
  • Wamashudu Sigogo

    (The Independent Institute of Education, IIEMSA)

  • Rodney Mushininga

    (IIEMSA)

Abstract

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. Key Words:Swarm intelligence, Cyber Threats, Network Data, Intrusion Detetion Systems

Suggested Citation

  • Wamashudu Sigogo & Rodney Mushininga, 2025. "Modelling intrusion detection systems using swarm intelligence," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 14(1), pages 222-236, January.
    • Handle: RePEc:rbs:ijbrss:v:14:y:2025:i:1:p:222-236
    DOI: 10.20525/ijrbs.v14i1.3582
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