Adversarial Models Towards Data Availability and Integrity of Distributed State Estimation for Industrial IoT-Based Smart Grid

TL;DR

This paper investigates security vulnerabilities in distributed state estimation for smart grids, proposing novel two-stage cyberattack strategies that significantly disrupt data integrity and availability, with experimental validation on standard systems.

Contribution

It introduces new adversarial attack models targeting data availability and integrity in industrial IoT-based smart grids, enhancing understanding of potential security risks.

Findings

Two-stage cyberattack causes approximately 34.74% estimation error

Attacks prevent data exchange and inject falsified data

Significant impact compared to normal operation error levels

Abstract

Security issue of distributed state estimation (DSE) is an important prospect for the rapidly growing smart grid ecosystem. Any coordinated cyberattack targeting the distributed system of state estimators can cause unrestrained estimation errors and can lead to a myriad of security risks, including failure of power system operation. This article explores the security threats of a smart grid arising from the exploitation of DSE vulnerabilities. To this aim, novel adversarial strategies based on two-stage data availability and integrity attacks are proposed towards a distributed industrial Internet of Things-based smart grid. The former's attack goal is to prevent boundary data exchange among distributed control centers, while the latter's attack goal is to inject a falsified data to cause local and global system unobservability. The proposed framework is evaluated on IEEE standard 14-bus system and benchmarked against the state-of-the-art research. Experimental results show that the proposed two-stage cyberattack results in an estimated error of approximately 34.74% compared to an error of the order of 10^-3 under normal operating conditions.