Optimal Data Attacks on Power Grids: Leveraging Detection & Measurement Jamming

TL;DR

This paper introduces a novel framework for power grid data attacks that combines bad-data injection and jamming, revealing new attack strategies with lower costs and broader feasibility, supported by graph theory and simulations.

Contribution

It presents a new 'detectable jamming' attack model, analyzes its cost regions, and develops an efficient approximation algorithm for attack design.

Findings

Detectable jamming attacks can alter state estimation despite detection checks.

Jamming costs influence attack design only if below half the injection cost.

The proposed algorithm effectively constructs attack vectors in IEEE test systems.

Abstract

Meter measurements in the power grid are susceptible to manipulation by adversaries, that can lead to errors in state estimation. This paper presents a general framework to study attacks on state estimation by adversaries capable of injecting bad-data into measurements and further, of jamming their reception. Through these two techniques, a novel `detectable jamming' attack is designed that changes the state estimation despite failing bad-data detection checks. Compared to commonly studied `hidden' data attacks, these attacks have lower costs and a wider feasible operating region. It is shown that the entire domain of jamming costs can be divided into two regions, with distinct graph-cut based formulations for the design of the optimal attack. The most significant insight arising from this result is that the adversarial capability to jam measurements changes the optimal 'detectable jamming' attack design only if the jamming cost is less than half the cost of bad-data injection. A polynomial time approximate algorithm for attack vector construction is developed and its efficacy in attack design is demonstrated through simulations on IEEE test systems.