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

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.