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

Cryptography and Security;Systems and Control;Systems and Control