A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf
.
Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security
2024
Journal of Electrical Systems
The research addresses the critical anomaly detection problem in Software-Defined Networking (SDN), a domain where network integrity and security are paramount. Employing the Isolation Forest algorithm, a machine learning model renowned for its efficacy in identifying outliers, the study systematically generates synthetic network traffic data to train and test the model's detection capabilities. The methodology encompasses simulating a range of contamination rates to reflect varying degrees of
doi:10.52783/jes.639
fatcat:vx2miuuvdrbevjspukymce3th4