Random sampling strategies for multivariate statistical process control to detect cyber-physical manufacturing attacks

Abstract

With the latest advances in computer and networking technologies, the threat of cyber-physical attacks against manufacturing systems is growing. Unlike traditional cyber-attacks, cyber-physical attacks are not limited to intellectual property theft and affect the physical world, which could be devastating to manufacturing, if they are undetected. Relying on traditional quality control to defend against these malicious attacks, manufacturers can choose to either closely monitor a large number of potential quality characteristics or only monitor a specific subset of the characteristics. However, the former choice may be impractical when a large number of potential characteristics exists, whereas the latter might be susceptible to an intelligently designed attack that targets unmonitored characteristics. Therefore, a novel random variable-selection approach that is both resilient to malicious cyber-physical attacks and sensitive to shifts over a small subset of characteristics is proposed in this work. Such an approach is based upon random sampling strategies when using multivariate Hotelling T² control charts. To assess its usefulness, the proposed approach was compared to an established variable-selection method, using a simplified cost model. The obtained results show that the proposed approach is both cost-effective and well-suited for industrial applications where the number of quality characteristics to monitor is quite significant.

Keywords:

• cyber-physical security
• manufacturing systems
• multivariate statistical process control
• quality control
• random sampling

About the authors

Ahmad E. Elhabashy has received his PhD degree in Industrial and Systems Engineering from Virginia Tech in 2018 and both his BSc and MSc degrees in Production Engineering from Alexandria University, Egypt, in 2009 and 2012, respectively. Ahmad is currently working at the Production Engineering Department at Alexandria University. His research interests include quality control, production planning and control, modeling of industrial systems, and optimization, particularly in manufacturing context.

Romina Dastoorian is a PhD candidate of Industrial Engineering at Western Michigan University. She has obtained her bachelor’s degree in Industrial and System Engineering from Iran University of Science and Technology (IUST) in 2012. Her current research includes statistical process control, profile monitoring, and additive manufacturing.

Lee J. Wells received the BSc and the MSc degrees in Mechanical Engineering from Michigan Technological University, Houghton, Michigan, in 2005 and 2008, respectively, and the PhD degree in Industrial and Systems Engineering from Virginia Tech, Blacksburg, Virginia in 2013. He is currently an Assistant Professor in the Department of Industrial and Entrepreneurial Engineering & Engineering Management, Western Michigan University, Kalamazoo, Michigan. His research interests include cyber-physical security for advanced manufacturing, quality control for data-rich manufacturing environments, and statistical process control.

Jaime A. Camelio is currently the Associate Dean for Research, Innovation, and Entrepreneurship at the University of Georgia College of Engineering. Previously, he was the Rolls-Royce Commonwealth professor for advanced manufacturing at the Grado Department of Industrial and Systems Engineering at Virginia Tech and led its Cyber-Physical Systems Security Manufacturing Group, which along with its industry partners and alliance with government agencies, looked to improve the resiliency of the manufacturing infrastructure. He holds a PhD in Mechanical Engineering and a MSc in Industrial Engineering from the University of Michigan and a BSc and an MSc degree in Mechanical Engineering from the Universidad Catolica de Chile. His research interests are in assembly systems, intelligent manufacturing, process monitoring and control, and cyber-physical security in manufacturing. He has authored or coauthored more than 70 technical papers and holds one patent.

Acknowledgments

The authors would like to thank Prof. William H. Woodall, at Virginia Tech, for his helpful suggestions on the manuscript’s early draft.

Notes

1 These shifts are sustained in magnitude, but transient in the choice of variables being attacked.

Additional information

Funding

This research work was partially supported by the National Science Foundation (NSF) grant CMMI-1436365 and Virginia Tech’s Cyber-Physical Security Systems Manufacturing (CPSSMFG) Group. However, any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.