Hybridization of Support Vector Machine with Cat Swarm Algorithm for Intrusion Detection

I. O. Oyefolahan*, S. Idris**, J. N. Ndunagu***
* Department of Information Technology, School of ICT, Federal University of Technology, Minna, Nigeria.
** Department of Computer Science, School of ICT, Federal University of Technology, Minna, Nigeria.
*** Department of Computer Science, National Open University of Nigeria, Abuja, Nigeria.
Periodicity:March - May'2020
DOI : https://doi.org/10.26634/jcom.8.1.17459


Intrusion detection system (IDS) like firewall, access control and encryption mechanisms no longer provide the much needed security for systems and computer networks. Current IDS are developed on anomaly detection which helps in identifying attacks both known and unknown. Unfortunately, these anomaly-based IDS features high false rate. In a bid to reduce this false alarm rate, this paper proposed an intrusion detection model based on Support Vector Machine (SVM) optimized with Cat swarm optimization (CSO) algorithm. Attribute reduction has been carried out based on Information Gain (IG) and classification has been performed based on the optimized Support vector. The result obtained shows that our model performs well with the least false alarm rate and good accuracy value compared with other classification algorithms evaluated using the same datasets.


Intrusion Detection, Support Vector Machine, Cat Swarm Optimization, Information Gain, NSL-KDD.

How to Cite this Article?

Oyefolahan, O. I., Idris, S., and Ndunagu, J. N.(2020). Hybridization of Support Vector Machine with Cat Swarm Algorithm for Intrusion Detection. i-manager's Journal on Computer Science, 8(1), 1-13. https://doi.org/10.26634/jcom.8.1.17459


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