Critical Manufacturing Sector

This research assesses the threat of cyber-physical attacks to manufacturing systems that change the design of a physical part, elude quality control measures, and result in part failure. This goal is achieved through the development of: a cyber-physical attack taxonomy, framework to assess levels of cyber-physical vulnerability, models to detect and diagnose the presence of attacks in real-time, and side-channel detection techniques specific to manufacturing.

Calibration, or accuracy control, of cyber-physical additive manufacturing systems relies on predictive models for geometric shape deformation. However, learning predictive models is made difficult by the wide variety of possible process conditions and shapes. In addition, resource constraints limit the manufacture of test shapes, which further impedes learning of deformation models for new shape varieties. A methodology that can make full use of data collected on different shapes and reduce the haphazard aspect of traditional learning techniques is necessary in this context.