SCADA system testbed for cybersecurity research using machine learning approach

In this 2018 article published in Future Internet, Teixeira et al. test five machine learning algorithms in a supervisory control and data acquisition (SCADA) system testbed to determine whether or not machine learning is useful in cybersecurity research. Given the increasing number and sophistication of network-based attacks on industrial and research sensor networks (among others), the authors assessed the prior research of others in the field and integrated their findings into their own SCADA testbed dedicated to controlling a water storage tank. After training the algorithms and testing the system with attacks, they concluded that the Random Forest and Decision Tree algorithms were best suited for the task, showing " the feasibility of detecting reconnaissance attacks in [industrial control system] environments."

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