Preferential Cyber Defense for Power Grids

Abstract

The integration of computing and communication capabilities into the power grid has led to vulnerabilities enabling attackers to launch cyberattacks on the grid. The resources that can be deployed to protect a power grid are limited, rendering the need to impose preferences and priorities in optimal resource allocation. Due to the complexity of modern power grids, exploitation of machine learning is desired for developing optimal preferential cybersecurity defense strategies, where choosing a suitable mathematical framework to describe preference satisfaction and articulating a specific machine-learning method are key. We develop a reinforcement-learning approach with the objective of satisfying the preferences as quantitatively described by linear temporal logic. To characterize the preferences, we exploit a probabilistic planning approach that transforms preference satisfaction into a mixed-integer programming (MIP) problem, incorporate MIP into the resource-allocation problem, and use reinforcement learning to obtain the optimal policy. Due to the time-varying nature of the problem, the transformation needs to be carried out and MIP is to be solved at each time step. Utilizing the benchmark W&W 6-bus power-grid network, we validate our preferential machine-learning framework to defend the system against attacks under limited resources. Although our framework is computationally intensive at the present, it provides a stepping stone toward developing more efficient machine-learning frameworks to preferentially defend large cyberphysical systems.