Detecting False Data Injection in a Large-Scale Water Distribution Network

Abstract

Utility companies rely on accurate data (e.g. energy or water usage) to monitor and determine the pricing and distribution of resources. In most cities, a utility company tends to service a large number of houses in that city. These houses may not be concentrated in a neighborhood and this can make it difficult for them to manage because of the different patterns of water usage that exist in various neighborhoods. An adversary can take advantage of this by injecting false data into a subset of the houses such that the difference will not be noticed by the utility. False data injection (FDI) attacks compromise the integrity of the data, leading to inaccurate decision-making and potential water resource wastage. To address this problem, this research aims to study a clustering algorithm that leverages graph theory to cluster houses with similar water usage patterns in a city. After this, an FDI detection model is run on each cluster to identify any attack.