Intrusion Detection System for SCADA Platforms through Machine Learning Algorithms

SCADA systems, an acronym for Supervisory Control And Data Acquisition (supervisory, Control and data acquisition), are control networks that allow the monitoring and management of industrial processes remotely. In the beginning, their top priority was the availability of information bidirectionally between the control station and the remote units; however, the growing escalation of industrial systems, as well as internet connectivity has led to reconsider the old paradigm to give more importance to the issue of security, in order to avoid a possible cyber-attack endangers the functioning of the SCADA system. These attacks can affect even the industry and put into risk the security of a country. The present paper proposes the creation of an adaptive system for the detection of intruders or IDS (for its acronym in English) on SCADA networks, through the use of supervised machine learning techniques, oriented to the analysis of variables of the control devices. A support vector of type "Class One" machine and a test lab, allowed the validation of the proposed model.