Analyzing attacks on ICS/SCADA wind farm physical testbed with ML.

The use of Supervisory Control and Data Acquisition (SCADA) systems in wind farms has increased significantly in recent years. These SCADA systems are vulnerable to cyberattacks, which can lead to loss of integrity and availability of the device resources and financial losses. In this research, we have analyzed the effectiveness of machine learning techniques in detecting and mitigating cyberattacks on wind physical testbed. A dataset with simulated attacks on a physical wind testbed created by us is used. The dataset consists of network traffic, sensor readings, and control commands. To classify the attacks and measure their accuracy, multiple machine learning algorithms are trained on this dataset, including but not limited to deep neural networks (DNN), random forest classifier (RFC), decision tree classifier (DTC) and support vector machines (SVM). The outcomes of this research offer valuable insights into improving cybersecurity measures and resiliency for wind testbeds and other critical infrastructure systems. [ABSTRACT FROM AUTHOR]