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The electric grid modernization effort relies on the extensive deployment ofmicrogrid (MG) systems. MGs integrate renewable resources and energy storagesystems, allowing to generate economic and zero-carbon footprint electricity,deliver sustainable energy to communities using local energy resources, andenhance grid resilience. MGs as cyberphysical systems include interconnecteddevices that measure, control, and actuate energy resources and loads. Foroptimal operation, cyberphysical MGs regulate the onsite energy generationthrough support functions enabled by smart inverters. Smart inverters, beingconsumer electronic firmware-based devices, are susceptible to increasingsecurity threats. If inverters are maliciously controlled, they cansignificantly disrupt MG operation and electricity delivery as well as impactthe grid stability. In this paper, we demonstrate the impact ofdenial-of-service (DoS) as well as controller and setpoint modification attackson a simulated MG system. Furthermore, we employ custom-built hardwareperformance counters (HPCs) as design-for-security (DfS) primitives to detectmalicious firmware modifications on MG inverters. The proposed HPCs measureperiodically the order of various instruction types within the MG inverter'sfirmware code. Our experiments illustrate that the firmware modifications aresuccessfully identified by our custom-built HPCs utilizing various machinelearning-based classifiers.

Hardware-Assisted Detection of Firmware Attacks in Inverter-Based Cyberphysical Microgrids