Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach

TL;DR

This paper introduces a rare-event sampling method to efficiently identify load-altering attacks on power grids that could cause critical failures, providing insights into attack vectors and system vulnerabilities.

Contribution

The work presents a novel rare-event sampling approach tailored for power grid security, significantly reducing computational effort in identifying impactful load-altering attacks.

Findings

Identified key victim nodes for impactful attacks

Revealed how spatial distribution of LAAs triggers emergency responses

Demonstrated efficiency of the sampling method in simulations

Abstract

By manipulating tens of thousands of internet-of-things (IoT) enabled high-wattage electrical appliances (e.g., WiFi-controlled air-conditioners), large-scale load-altering attacks (LAAs) can cause severe disruptions to power grid operations. In this work, we present a rare-event sampling approach to identify LAAs that lead to critical network failure events (defined by the activation of a power grid emergency response (ER)). The proposed sampler is designed to "skip" over LAA instances that are of little interest (i.e., those that do not trigger network failure), thus significantly reducing the computational complexity in identifying the impactful LAAs. We perform extensive simulations of LAAs using the Kundur two-area system (KTAS) power network while employing the rare-event sampler. The results help us identify the victim nodes from which the attacker can launch the most impactful attacks and provide insights into how the spatial distribution of LAAs triggers the activation of ERs.