# Can Attackers with Limited Information Exploit Historical Data to Mount Successful False Data Injection Attacks on Power Systems?

## TL;DR

This paper demonstrates that attackers with limited knowledge can exploit historical data and linear regression models to successfully perform false data injection attacks on power systems, causing potential overloads without detection.

## Contribution

It introduces a novel attack model using historical data and regression to enable unobservable FDI attacks with limited system information.

## Key findings

- Attackers can successfully overload target lines using limited data.
- The proposed model is effective on IEEE test systems.
- Limited information does not prevent successful FDI attacks.

## Abstract

This paper studies physical consequences of unobservable false data injection (FDI) attacks designed only with information inside a sub-network of the power system. The goal of this attack is to overload a chosen target line without being detected via measurements. To overcome the limited information, a multiple linear regression model is developed to learn the relationship between the external network and the attack sub-network from historical data. The worst possible consequences of such FDI attacks are evaluated by solving a bi-level optimization problem wherein the first level models the limited attack resources, while the second level formulates the system response to such attacks via DC optimal power flow (OPF). The attack model with limited information is reflected in the DC OPF formulation that only takes into account the system information for the attack sub-network. The vulnerability of this attack model is illustrated on the IEEE 24-bus RTS and IEEE 118-bus systems.