IoT-Enabled Decentralized Moving Target Defense for Enhancing Privacy in Microgrid Control

Abstract

This paper proposes the Decentralized Moving Target Defense via Data Replication (DMTDR) framework, which is a non-disruptive privacy preserving mechanisms that exploits the flexibility and low-cost of IoT devices to enhance the privacy of microgrids without deteriorating their performance. The DMTDR framework consist of the use of IoT devices to replicate relevant sensory and control signals, which are randomly transmitted through independent communication channels, limiting the adversary’s ability to learn details about the system due to the intermittent availability of the information. Since at least one replica is transmitted at each time instant, the sensor and control data is always available for the receiver, such that the system performance is not affected. Different to most moving target defense (MTD) approaches found in the literature, the proposed DMTDR does not require central coordination but instead it can be applied to any monitoring or control device that utilizes a communication channel. The proposed framework is demonstrated on an islanded microgrid scenario with energy storage systems and distributed generators. The results show that the proposed framework enhances privacy for different attacker’s models by i) decreasing the ability of adversaries to learn the system model ii) and impact the accuracy of a knowledgeable attacker to compute a Kalman filter state estimation.