Professor Moon and doctoral candidate Espinoza-Zelaya propose a systemic approach to estimate threat severity of cyber-attacks against cyber-manufacturing. The threat estimate system consists of three variables: disruption potential, economic impact, and non-tangible losses. While the severity assessment cannot be used in advance of attack, worst-case estimates can still be performed. The high degree of uncertainty in cyberattacks against physical systems has made assessment generally intractable, but a standardized threat estimate system is highly valuable to a variety of industries. The authors have defined a resilient cyber-manufacturing system (CMS) as one that can prevent, detect, reduce, and recover from the adverse effects of a cyber-attack. A machine learning algorithm was implemented to detect corrupted orders sent to a CMS through illicit access. Then, a capacity utilization function was used to determine how the CMS should behave. Estimating threat and building resiliency into CMS against cyberattacks has critical applications to current and future infrastructure.
