Mitigating insider’s threats using support vector machine and k-nearest Neighbour

Maureen I. Akazue *, Nkiru Queen Muka and Abel E. Edje

Department of Computer Science, Delta State University, Abraka, Nigeria.
 
Review
International Journal of Science and Research Archive, 2024, 12(01), 2626–2635
Article DOI: 10.30574/ijsra.2024.12.1.1110
Publication history: 
Received on 08 May 2024; revised on 17 June 2024; accepted on 20 June 2024
 
Abstract: 
Addressing insider’s threats is a critical challenge in organizational security. This study presents the development and evaluation of a hybrid machine learning model aimed at enhancing insider’s threat detection effectiveness. The escalating risks associated with insider’s threats necessitated advance detection mechanisms to mitigate potential breaches. Leveraging the strengths of multiple individual models, including Support Vector Machine (SVM) and K-nearest Neighbour (KNN), the hybrid model addressed this challenge by improving detection accuracy while minimizing false positives. Through rigorous evaluation, the hybrid model demonstrates remarkable performance, achieving an accuracy of 99%, with precision, recall, and F1 score of 99%, 98%, and 97% respectively. By providing a robust solution to insider’s threat detection, the hybrid model offers organizations a promising approach to fortify security measures and safeguard against potential breaches.
 
Keywords: 
Support Vector Machine (SVM); K-Nearest Neighbour (KNN); Hybrid Model, Machine Learning; Insider’s Threat and Jupyter Notebook
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0