Modelling intrusion detection systems using swarm intelligence

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.

Keywords: {'name': 'Swarm intelligence, Cyber Threats, Network Data, Intrusion Detetion Systems'}

Authors & affiliations

Wamashudu Sigogo
Affiliation not supplied

Rodney Mushininga
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.1007/s10845-010-0393-4

Al Salami, N. M. A. (2009). Ant colony optimization algorithm. UbiCC Journal, 4(3), 823–826.

DOI: 10.1109/ACCESS.2018.2820092

DOI: 10.2991/ijcis.d.210105.001

DOI: 10.1155/2015/574589

Areff, A. (2022). TransUnion believes a breach of 54 million SA records unrelated to the current hack. News24. Retrieved from https://www.news24.com/fin24/companies/transunion-hack-banks-trying-to-protect-accounts-but-warn-of-tricky-impersonators-20220319

Axelsson, S. (2000). Intrusion detection systems: A survey and taxonomy. Retrieved from https://www.researchgate.net/publication/2597023_Intrusion_Detection_Systems_A_Survey_and_Taxonomy.

Bae, C., Yeh, W., & Hsieh. (2012). A novel anomaly-network intrusion detection system using ABC algorithms. International Journal of Innovative Computing, Information and Control, 8.

DOI: 10.1016/j.procs.2015.07.040

DOI: 10.1016/j.procs.2019.11.002

Bhandari, P. (2023). Triangulation in research. Scribbr. Retrieved from https://www.scribbr.com/methodology/triangulation/

CheckPoint. (n.d.). Intrusion detection system.

Cilliers, F., Davis, C., & Bezuidenhout, R. (2014). Research matters (1st ed.). Cape Town: Juta and Company Ltd.

DOI: 10.1088/1742-6596/1566/1/012045

Feigl, H. (2023). Positivism philosophy. Britannica. Retrieved from https://www.britannica.com/topic/positivism

DOI: 10.1007/s11831-021-09694-4

George, T. (2023). What is qualitative observation? Definition and examples. Scribbr. Retrieved from https://www.scribbr.com/methodology/qualitative-observation/#:~:text=Qualitative%20observation%20is%20a%20research,smell%2C%20taste%2C%20or%20feel.

DOI: 10.1016/j.icte.2018.01.014

DOI: 10.1016/j.comnet.2018.02.028

Haq, N. F., Rafni, M., Onik, A. R., Shah, F. M., Hridoy, M. A. K., & Farid, D. M. (2015). Application of machine learning approaches in intrusion detection systems: A survey. International Journal of Advanced Research in Artificial Intelligence, 1(10), 1–10.

DOI: 10.2172/425295

DOI: 10.4156/jdcta.vol3.issue1.janakiraman

DOI: 10.4249/scholarpedia.6915

DOI: 10.1007/s10898-007-9149-x

DOI: 10.1007/s10462-012-9328-0

DOI: 10.1186/s42400-019-0038-7

DOI: 10.1016/j.jnca.2012.09.004

Lutkevich, B. (n.d.). Intrusion detection system (IDS). TechTarget. Retrieved from https://www.techtarget.com/searchsecurity/definition/intrusion-detection-system

DOI: 10.1007/s10586-017-0971-8

DOI: 10.1016/j.jksuci.2018.03.011

DOI: 10.1007/978-3-319-32213-1_27

Melampy, P. (2018). Networking anomalies. Network World. Retrieved from https://www.networkworld.com/article/3284939/networking-anomalies.html.