Abstract : Vulnerability detection in Supervisory Control and Data Acquisition (SCADA) network of a Smart Factory (SF) is a high-priority research area in the cyber-security domain. Choosing an efficient Machine Learning (ML) algorithm for intrusion detection is a huge challenge. This study performed an investigative analysis into the classification ability of various ML models leveraging public cyber-security datasets to determine the best model. Based on the performance evaluation, all adaptions of Decision Tree (DT) and KNN in terms of accuracy, training time, MCE, and prediction speed are the most suitable ML for resolving security issues in the SCADA system.

Paper History
First Submitted to ICT Express
Submitted: 2021.08.20
Rejected: 2021.09.06

Resubmitted to IEEE Access After Review Comments effected
Submission: October 15, 2021
Acceptance: November 07, 2021
DOI : 10.1109/ACCESS.2021.3127560