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."
This is a collection of free online introductory laboratory informatics courses developed by the Centers for Disease Control and Prevention (CDC) and the Association of Public Health Laboratories (APHL). The first two courses follow the journey of a specimen through the laboratory and explores the generation and transmission of data and results within and outside of the laboratory. A third course on laboratory informatics systems is in development. Participants can earn Professional Acknowledgment for Continuing Education® (P.A.C.E.®) credits upon completion.