Data Attacks on Power System State Estimation: Limited Adversarial Knowledge vs. Limited Attack Resources

TL;DR

This paper investigates how limited knowledge and resources of adversaries affect the success and detectability of false data injection attacks on power system state estimation, highlighting vulnerabilities and attack impact.

Contribution

It generalizes data attacks to include both FDI and availability attacks under resource constraints and analyzes their impact and detectability in power systems.

Findings

Limited knowledge reduces attack success probability.

Certain measurements are more vulnerable under resource constraints.

Detection probability varies with attack knowledge and resources.

Abstract

A class of data integrity attack, known as false data injection (FDI) attack, has been studied with a considerable amount of work. It has shown that with perfect knowledge of the system model and the capability to manipulate a certain number of measurements, the FDI attacks can coordinate measurements corruption to keep stealth against the bad data detection. However, a more realistic attack is essentially an attack with limited adversarial knowledge of the system model and limited attack resources due to various reasons. In this paper, we generalize the data attacks that they can be pure FDI attacks or combined with availability attacks (e.g., DoS attacks) and analyze the attacks with limited adversarial knowledge or limited attack resources. The attack impact is evaluated by the proposed metrics and the detection probability of attacks is calculated using the distribution property of data with or without attacks. The analysis is supported with results from a power system use case. The results show how important the knowledge is to the attacker and which measurements are more vulnerable to attacks with limited resources.