Hardware-assisted detection of firmware attacks in inverter-based cyberphysical microgrids

The electric grid modernization effort relies on the extensive deployment of microgrid (MG) systems. MGs integrate renewable resources and energy storage systems, allowing to generate economic zero-carbon footprint electricity, deliver sustainable communities using local resources, enhance resilience. as cyberphysical systems include interconnected devices that measure, control, actuate loads. For optimal operation, regulate onsite generation through support functions enabled by smart inverters. Smart inverters, being consumer electronic firmware-based devices, are susceptible increasing security threats. If inverters maliciously controlled, they can significantly disrupt MG operation electricity delivery well impact stability. In this paper, we demonstrate denial-of-service (DoS) controller setpoint modification attacks a simulated system. Furthermore, employ custom-built hardware performance counters (HPCs) design-for-security (DfS) primitives detect malicious firmware modifications proposed HPCs measure periodically order various instruction types within inverter’s code. Our experiments illustrate successfully identified our utilizing machine learning-based classifiers.